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Category TheoryFunctional AnalysisTopology·Captain: mikedeng1

Gelfand-type duality for commutative von Neumann algebras: The Opposite Category of Commutative von Neumann Algebras Is Equivalent to Measurable Locales, Hyperstonean Locales and Spaces, and CSLEMSResearch Paper

Motivation

Gelfand duality identifies compact Hausdorff spaces with commutative unital C*-algebras: a space XXX corresponds to the algebra C(X)C(X)C(X) of continuous complex functions, and continuous maps correspond contravariantly to unital *-homomorphisms (Gelfand 1941; in categorical form, Negrepontis 1971). Measure theory has an analogous expectation. A measurable space with a σ-ideal of negligible sets yields the commutative von Neumann algebra L∞L^\inftyL∞ of bounded measurable functions modulo equality almost everywhere, and every commutative von Neumann algebra should arise in this way. Making that expectation precise as an equivalence of categories requires deciding which measurable spaces to allow and which maps between them to identify.

Timeline. Stone (1936–1937) established the duality between Boolean algebras and Stone spaces, and between complete Boolean algebras and extremally disconnected ones. Segal (1951) introduced localizable measure spaces. Dixmier (1951) characterized the Gelfand spectra of commutative von Neumann algebras as the hyperstonean spaces. Loomis (1947) and Sikorski (1948) represented σ-complete Boolean algebras as measurable sets modulo meager sets on their Stone spaces. Von Neumann (1932), Maharam (1958), Ionescu Tulcea (1965), Vesterstrøm–Wils (1969), Edgar (1976) and Graf (1980) gave criteria for lifting homomorphisms of measure algebras to point maps. Pavlov (arXiv:2005.05284, J. Pure Appl. Algebra) assembled these ingredients into one equivalence of categories. That equivalence is the subject of this mission.

Setting

An enhanced measurable space (X,M,N)(X,M,N)(X,M,N) is a set XXX, a σ-algebra MMM of subsets of XXX, and a σ-ideal N⊆MN\subseteq MN⊆M of the power set 2X2^X2X, the negligible sets. A premap f ⁣:(X,M,N)→(X′,M′,N′)f\colon(X,M,N)\to(X',M',N')f:(X,M,N)→(X′,M′,N′) is a map defined on a conegligible subset of XXX with f∗M′⊆Mf^*M'\subseteq Mf∗M′⊆M and f∗N′⊆Nf^*N'\subseteq Nf∗N′⊆N. Two premaps are weakly equal almost everywhere, f≈gf\approx gf≈g, when f∗m⊕g∗m∈Nf^*m\oplus g^*m\in Nf∗m⊕g∗m∈N for every m∈M′m\in M'm∈M′. The space is compact if some compact class K⊆MK\subseteq MK⊆M (a family with the finite intersection property for all subfamilies) has a non-negligible member inside every non-negligible measurable set. It is strictly localizable if it is isomorphic, through premaps, to a coproduct of a small family of spaces carrying faithful finite measures. CSLEMS is the category of compact strictly localizable spaces with ≈\approx≈-classes of premaps.

A frame is a complete lattice in which finite meets distribute over arbitrary joins; the category of locales is the opposite of the category of frames. A complete Boolean algebra is localizable if 111 is the supremum of the elements aaa for which aAaAaA carries a finite faithful continuous valuation. MLoc, the category of measurable locales, is the full subcategory of locales on the localizable Boolean algebras. A hyperstonean locale is a Stonean locale (coherent, regular, ¬x∨¬¬x=1\neg x\vee\neg\neg x=1¬x∨¬¬x=1) in which every nonzero open has a positive normal valuation not vanishing on it. A hyperstonean space is a sober space whose locale of opens is hyperstonean. HStoneanLoc and HStonean take open maps as morphisms. CVNA is the category of commutative von Neumann algebras (C*-algebras with a predual) and normal unital *-homomorphisms.

Formalization targets

Goal: Theorem 1.1

HStonean≃HStoneanLoc,HStoneanLoc≃MLoc,CSLEMS≃MLoc,MLoc≃CVNAop.\mathsf{HStonean}\simeq\mathsf{HStoneanLoc},\qquad \mathsf{HStoneanLoc}\simeq\mathsf{MLoc},\qquad \mathsf{CSLEMS}\simeq\mathsf{MLoc},\qquad \mathsf{MLoc}\simeq\mathsf{CVNA}^{\mathrm{op}}.HStonean≃HStoneanLoc,HStoneanLoc≃MLoc,CSLEMS≃MLoc,MLoc≃CVNAop.

The goal is the existence of the four equivalences. It does not fix the functors.

The four equivalences with their functors

  • Proposition 2.72: the equivalence HStonean≃HStoneanLoc\mathsf{HStonean}\simeq\mathsf{HStoneanLoc}HStonean≃HStoneanLoc is implemented by the locale of opens Ω\OmegaΩ.
  • Proposition 2.73: the equivalence HStoneanLoc≃MLoc\mathsf{HStoneanLoc}\simeq\mathsf{MLoc}HStoneanLoc≃MLoc is implemented by the compact opens COpen\mathrm{COpen}COpen.
  • Theorem 3.20: the equivalence MLoc≃CVNAop\mathsf{MLoc}\simeq\mathsf{CVNA}^{\mathrm{op}}MLoc≃CVNAop has inverse ProjLoc\mathsf{ProjLoc}ProjLoc, given by projections and restriction.
  • Theorem 5.19: the equivalence CSLEMS≃MLoc\mathsf{CSLEMS}\simeq\mathsf{MLoc}CSLEMS≃MLoc is implemented by ML\mathsf{ML}ML, the functor (X,M,N)↦M/N(X,M,N)\mapsto M/N(X,M,N)↦M/N, f↦f∗f\mapsto f^*f↦f∗.

Supporting milestones

The supporting milestones, in proof order:

  • normal valuations versus continuous valuations on compact opens (Lemma 2.58);
  • localizability through valuations (Lemma 2.53);
  • completeness of ProjAlg(A)\mathsf{ProjAlg}(A)ProjAlg(A) (Proposition 3.8);
  • A∗A_*A∗​ as continuous valuations (Proposition 3.9);
  • ProjAlg\mathsf{ProjAlg}ProjAlg as a functor (Lemma 3.11);
  • σ-complete algebras with faithful valuations (Lemma 4.55);
  • strict localizability implies localizability (Proposition 4.54);
  • compactness of induced subspaces (Lemma 4.61);
  • measurable images (Proposition 4.65);
  • TM\mathrm{TM}TM on hyperstonean spaces (Proposition 5.4);
  • ML\mathsf{ML}ML on morphisms (Lemma 5.11);
  • the isomorphism (X,M,N)≅Spec(ML(X,M,N))(X,M,N)\cong\mathrm{Spec}(\mathsf{ML}(X,M,N))(X,M,N)≅Spec(ML(X,M,N)) (Proposition 5.16).

Significance

The result. Theorem 1.1 replaces commutative von Neumann algebras, a class of operator algebras, by measurable locales, which are pointless spaces. It also gives a point-set model, CSLEMS, in which morphisms are measurable maps modulo a null relation. With it, constructions on commutative von Neumann algebras can be carried out measure-theoretically and transported back: products, pushforwards of measures, and the identification of the predual with valuations. It also explains why the obvious choices fail. With localizable instead of strictly localizable spaces, with equality almost everywhere instead of weak equality, or without compactness, the measure-theoretic category is not dual to CVNA (Remarks 4.23 and 5.18).

Formalization. The theorem is proved in the paper. None of the five categories, and none of the equivalences, has a machine-checked form in Mathlib or on the platform. Mathlib has W*-algebras (WStarAlgebra), locales, Stonean spaces and Baire-measurable sets, but no normal homomorphisms, measure algebras or lifting theorems. The published module QAlgorithms_Papers_Independent_Support21_P20 formalizes this paper's valuations, enhanced measurable spaces and premaps, and poses several lemmas, three of which are milestones here. Completing the mission means formalizing Stone and Stonean duality for locales, the spectral theory of commutative von Neumann algebras through projections, and the von Neumann–Maharam lifting theorem for strictly localizable spaces.

Difficulty

The equivalences in §2 restrict classical dualities and are mostly bookkeeping once those dualities are available. Even so, Mathlib's Stone duality is not stated for locales with open maps. The central difficulty is Proposition 5.16. An object of CSLEMS must be isomorphic, through point maps, to the Stone spectrum of its own measure algebra. In one direction this needs a lifting M/N→MM/N\to MM/N→M that is a Boolean homomorphism, and that uses strict localizability through the von Neumann–Maharam theorem. In the other direction it needs a point map from the spectrum back to XXX, constructed from the compact class. The natural first idea, choosing representatives class by class, does not give a homomorphism: liftings exist only through a transfinite construction. A second difficulty is Lemma 5.11. A Boolean homomorphism induced by a measurable map preserves countable suprema, but in general it does not preserve arbitrary ones (Remark 5.12). Compactness and the measurable image of Proposition 4.65 are what rule this out.

Formalization scope

All five categories live in one universe, with objects in Type u. The encoding commits to these conventions:

  • CSLEMS. Morphisms are partial premaps (X → Option Y) modulo weak equality almost everywhere. Theorem 1.1's bullet speaks of maps of sets, but with total maps the theorem is false: (pt,2pt,2pt)(\mathrm{pt},2^{\mathrm{pt}},2^{\mathrm{pt}})(pt,2pt,2pt) has no map to ∅\varnothing∅ (Remarks 4.9, 4.15). Strict localizability uses a small index family and isomorphism in PreEMS; the faithful finite measures of its σ-finite pieces are countably additive maps M→CM\to\mathbb CM→C with absolutely convergent sums (Remark 4.29), because Definition 4.28's saturation clause alone would also admit merely finitely additive ones. Compactness quantifies over arbitrary subfamilies, not countable ones.
  • MLoc. The full subcategory of Mathlib's Locale on localizable Boolean algebras.
  • HStoneanLoc and HStonean. Morphisms are open frame maps (Definition 2.15) and open continuous maps. Hyperstonean spaces are sober spaces whose frame of opens is hyperstonean.
  • CVNA. Commutative CStarAlgebras with WStarAlgebra. Normality is stated as "pulls normal functionals back to normal functionals", which is equivalent to Definition 3.3 by Sakai's uniqueness of the predual.
  • No nontriviality. The empty space, the one-element Boolean algebra and the zero algebra are allowed.

The equivalence milestones assert an equivalence together with a description of its functor. They never take a functor as a hypothesis, so none of the statements reduces to rfl or to an instance argument.

Reusable parts:

  • the CSLEMS category, with the category laws of Propositions 4.7 and 4.14 proved inline;
  • the quotient M/NM/NM/N and the pull-back;
  • TM\mathrm{TM}TM and the Stone spectrum;
  • the projection Boolean algebra.

Contributions are welcome at every level: proofs of the milestones, Stone and Stonean duality for locales, and a lifting theorem.

Selected references

  • D. Pavlov, Gelfand-type duality for commutative von Neumann algebras, J. Pure Appl. Algebra 226 (2022); arXiv:2005.05284v3. https://arxiv.org/abs/2005.05284
  • J. Dixmier, Sur certains espaces considérés par M. H. Stone, Summa Brasil. Math. 2 (1951), 151–182.
  • M. H. Stone, Algebraic characterizations of special Boolean rings, Fund. Math. 29 (1937), 223–303. https://eudml.org/doc/212943
  • I. E. Segal, Equivalences of measure spaces, Amer. J. Math. 73 (1951), 275–313. https://doi.org/10.2307/2372178
  • D. Maharam, On a theorem of von Neumann, Proc. Amer. Math. Soc. 9 (1958), 987–994. https://doi.org/10.1090/S0002-9939-1958-0105479-6
  • D. H. Fremlin, Measure Theory, Vol. 3 (Measure algebras), Torres Fremlin, 2002.
  • P. T. Johnstone, Stone Spaces, Cambridge University Press, 1982.
  • S. Sakai, C*-algebras and W*-algebras, Springer, 1971.
22 thms3 active usersReviewed
ProbabilityQuantum InformationRandom Matrix Theory·Captain: mikedeng1

Asymptotics of random density matrices: The Rescaled Empirical Spectral Measure of an Induced Random Density Matrix Converges Almost Surely to the Marchenko–Pastur LawResearch Paper

Motivation

In quantum mechanics, a system that interacts with an environment is described by a density matrix: a positive semidefinite complex matrix of trace one. When nothing is known about the joint state of the system and its environment, a natural model is to take that joint state to be a uniformly random pure state and to trace out the environment. The resulting law on density matrices, the induced measure of Życzkowski and Sommers (Życzkowski–Sommers 2001), is a standard model of a "typical" mixed state. It is used in quantum information to study the typical entanglement of bipartite pure states, as in Page's conjecture on average entropy (Page 1993).

Nechita (arXiv:quant-ph/0702154v3, Ann. Henri Poincaré 2007) studies the asymptotics of this ensemble. In the regime where the system dimension nnn and the environment dimension k(n)k(n)k(n) grow proportionally, the spectrum of the random density matrix, magnified by cncncn, has the same almost sure limit as the spectrum of a Wishart matrix: the Marchenko–Pastur law (Marchenko–Pastur 1967). The mission formalizes this result (Theorem 5) together with the chain of statements the paper uses to derive it.

Setting

Throughout, NC(0,1)\mathcal N_{\mathbb C}(0,1)NC​(0,1) denotes the law of Z=X+iYZ = X + iYZ=X+iY where X,YX, YX,Y are independent real Gaussians of mean 000 and variance 1/21/21/2, so that E∣Z∣2=1\mathbb E|Z|^2 = 1E∣Z∣2=1.

  • Wishart matrix. If XXX is an n×kn \times kn×k complex matrix with i.i.d. NC(0,1)\mathcal N_{\mathbb C}(0,1)NC​(0,1) entries, W=XX∗W = XX^*W=XX∗ is a Wishart matrix of parameters nnn and kkk.
  • Pure states. A pure state of Cm\mathbb C^mCm is a ray ∣ψ⟩|\psi\rangle∣ψ⟩: a nonzero vector up to a nonzero scalar factor. Its pure density matrix is ∣ψ⟩⟨ψ∣=ψψ∗/∥ψ∥2|\psi\rangle\langle\psi| = \psi\psi^*/\|\psi\|^2∣ψ⟩⟨ψ∣=ψψ∗/∥ψ∥2, a rank-one orthogonal projection. The set EmE_mEm​ of pure states carries a unique probability measure νm\nu_mνm​ invariant under all unitaries U∈U(m)U \in U(m)U∈U(m).
  • Induced measure. Let H=Cn\mathcal H = \mathbb C^nH=Cn (the system) and K=Ck\mathcal K = \mathbb C^kK=Ck (the environment). If ∣ψ⟩|\psi\rangle∣ψ⟩ has law νnk\nu_{nk}νnk​ on the pure states of H⊗K\mathcal H\otimes\mathcal KH⊗K, the law of
ρ=Tr⁡K ∣ψ⟩⟨ψ∣\rho = \operatorname{Tr}_{\mathcal K}\,|\psi\rangle\langle\psi|ρ=TrK​∣ψ⟩⟨ψ∣

is the induced measure μn,k\mu_{n,k}μn,k​ on n×nn\times nn×n density matrices. Here Tr⁡K\operatorname{Tr}_{\mathcal K}TrK​ is the partial trace over the environment.

  • Empirical spectral measure. For an n×nn \times nn×n matrix AAA with eigenvalues λ1(A),…,λn(A)\lambda_1(A), \dots, \lambda_n(A)λ1​(A),…,λn​(A) (counted with multiplicity) and a scale sss, put Ls(A)=1n∑iδsλi(A)L^s(A) = \frac1n\sum_i \delta_{s\lambda_i(A)}Ls(A)=n1​∑i​δsλi​(A)​.
  • Marchenko–Pastur law. For c>0c > 0c>0, with a=(c−1)2a = (\sqrt c-1)^2a=(c​−1)2 and b=(c+1)2b = (\sqrt c+1)^2b=(c​+1)2,
μc=max⁡{1−c,0} δ0+(x−a)(b−x)2πx 1[a,b](x) dx.\mu_c = \max\{1-c,0\}\,\delta_0 + \frac{\sqrt{(x-a)(b-x)}}{2\pi x}\,\mathbf 1_{[a,b]}(x)\,dx .μc​=max{1−c,0}δ0​+2πx(x−a)(b−x)​​1[a,b]​(x)dx.

Formalization targets

Goal: Theorem 5 (p. 14)

Let c∈(0,∞)c \in (0,\infty)c∈(0,∞), let k(n)k(n)k(n) be integers with k(n)/n→ck(n)/n \to ck(n)/n→c, and let ρn\rho_nρn​ have law μn,k(n)\mu_{n,k(n)}μn,k(n)​ for every nnn. Then, almost surely,

Ln=1n∑i=1nδc n λi(ρn)  ⟹  μcweakly.L_n = \frac1n\sum_{i=1}^n \delta_{c\,n\,\lambda_i(\rho_n)} \;\Longrightarrow\; \mu_c \quad \text{weakly}.Ln​=n1​i=1∑n​δcnλi​(ρn​)​⟹μc​weakly.

Milestones, in the order the proof uses them

  1. Proposition 1 (p. 4): νn\nu_nνn​ exists and is unique.
  2. Proposition 2, item 1 (p. 4): the class of a vector with law NCn(0,In)\mathcal N^n_{\mathbb C}(0,I_n)NCn​(0,In​) has law νn\nu_nνn​.
  3. Eq. (12) (p. 6): if the coordinates ψij\psi_{ij}ψij​ are arranged in an n×kn \times kn×k matrix XXX, then Tr⁡K∣ψ⟩⟨ψ∣=XX∗/Tr⁡(XX∗)\operatorname{Tr}_{\mathcal K}|\psi\rangle\langle\psi| = XX^*/\operatorname{Tr}(XX^*)TrK​∣ψ⟩⟨ψ∣=XX∗/Tr(XX∗).
  4. Lemma 1 (p. 6): if XXX has i.i.d. NC(0,1)\mathcal N_{\mathbb C}(0,1)NC​(0,1) entries, then XX∗/Tr⁡(XX∗)XX^*/\operatorname{Tr}(XX^*)XX∗/Tr(XX∗) has law μn,k\mu_{n,k}μn,k​.
  5. Eq. (24) (p. 9): the eigenvalues of W/Tr⁡WW/\operatorname{Tr} WW/TrW are λi/∑jλj\lambda_i/\sum_j\lambda_jλi​/∑j​λj​.
  6. Theorem 2 (p. 8, quoted from Haagerup–Thorbjørnsen): almost surely, 1n∑iδn−1λi(Wn)⇒μc\frac1n\sum_i\delta_{n^{-1}\lambda_i(W_n)} \Rightarrow \mu_cn1​∑i​δn−1λi​(Wn​)​⇒μc​ for Wishart matrices WnW_nWn​ of parameters nnn and k(n)k(n)k(n).
  7. Lemma 2, (45) (p. 13): Tr⁡Wn/(n k(n))→1\operatorname{Tr} W_n/(n\,k(n)) \to 1TrWn​/(nk(n))→1 almost surely.
  8. The perturbation step (p. 14, eqs. (51)–(53)): dilating the atoms of a weakly convergent sequence by factors tn→1t_n \to 1tn​→1 does not change a compactly supported weak limit.

Significance

The result. Theorem 5 says that the reduced state of a large random bipartite pure state has a deterministic spectral profile at scale 1/(cn)1/(cn)1/(cn). When c<1c < 1c<1, the environment is smaller than the system, and an atom of mass 1−c1-c1−c at 000 records that ρn\rho_nρn​ has rank k(n)<nk(n) < nk(n)<n. When c≥1c \ge 1c≥1, the spectrum fills the interval [a,b][a, b][a,b]. The result transfers the classical Wishart limit to the induced measure, and with it quantitative information on the typical entanglement spectrum of bipartite pure states.

Formalizing it. The theorem is proved on paper, and none of it is machine-checked. The mission produces the induced-measure layer in Lean: complex Gaussian matrices, the invariant law on pure states, the partial-trace construction of μn,k\mu_{n,k}μn,k​, and the Marchenko–Pastur law with its atom. Lemma 1 and eq. (12) connect these objects to Wishart matrices. The step that needs the most new library is Theorem 2, the Marchenko–Pastur theorem itself, which the paper quotes without proof. The perturbation step is posed as a statement about weak convergence. The paper argues it through convergence of moments, and in that argument weak convergence of LnWL_n^WLnW​ is used as if it gave convergence of moments. The statement posed here is true as stated and needs no moment control.

Difficulty

The reduction from Theorem 5 to Theorem 2 is short on paper. The work lies elsewhere.

  • Theorem 2 is a full random-matrix limit theorem. It needs a method that identifies the limit (moments, Stieltjes transforms, or free probability) and concentration strong enough to give almost sure convergence with no coupling between different nnn.
  • Proposition 1 needs the invariant measure on complex projective space and a uniqueness argument for it. Mathlib's Haar-measure uniqueness is stated for groups, not for homogeneous spaces of U(n)U(n)U(n).
  • Lemma 1 needs the unitary invariance of the complex Gaussian vector NCnk(0,I)\mathcal N^{nk}_{\mathbb C}(0,I)NCnk​(0,I) and the image of a law under partial trace.

The naive route of "taking moments" in the perturbation step fails: weak convergence does not give convergence of moments, so (52)–(53) cannot be compared without extra tightness or moment bounds.

Formalization scope

  • Pure states. Rays of Cm\mathbb C^mCm are represented by their rank-one projections (IsPureDensity: Hermitian, idempotent, trace one). A law on EmE_mEm​ is a probability measure on m×mm\times mm×m complex matrices carried by that set. Unitary invariance is invariance under ρ↦UρU∗\rho \mapsto U\rho U^*ρ↦UρU∗ (IsPureStateLaw).
  • Induced law. "ρ\rhoρ has law μn,k\mu_{n,k}μn,k​" is the predicate HasInducedLaw: the law of ρ\rhoρ is the image under the partial trace of some unitarily invariant law on pure states of Cn⊗Ck\mathbb C^n\otimes\mathbb C^kCn⊗Ck. Proposition 1 makes this law unique.
  • Indexing and partial trace. Cn⊗Ck\mathbb C^n\otimes\mathbb C^kCn⊗Ck is indexed by Fin n × Fin k with the environment on the right. The partial trace is the published WildeQIT.partialTraceRight. NC(0,1)\mathcal N_{\mathbb C}(0,1)NC​(0,1) is the published SpikedWishart.SoftEdge.stdComplexGaussian, with variance 1/21/21/2 per real coordinate.
  • Eigenvalues and convergence. Eigenvalues are roots of the characteristic polynomial, counted with multiplicity. Weak convergence is ∫f dLn→∫f dμc\int f\,dL_n \to \int f\,d\mu_c∫fdLn​→∫fdμc​ for all bounded continuous fff, with one null set for all fff.
  • Disclosed conventions. The hypotheses hold for n≥1n \ge 1n≥1 with k(n)≥1k(n)\ge 1k(n)≥1, since there are no pure states in dimension 000. All random matrices live on one probability space, and nothing is assumed about their joint law across different nnn: the paper never couples them, and the almost sure statements hold for every coupling.
  • Trivializations ruled out. Three trivializing formalizations are excluded:
    • stating the law of ρn\rho_nρn​ through the Wishart formula Wn/Tr⁡WnW_n/\operatorname{Tr}W_nWn​/TrWn​, which would make Lemma 1 a hypothesis;
    • dropping the atom of μc\mu_cμc​ or leaving the limit measure free;
    • stating "for each fff, almost surely" in place of one null set for all fff.

Solvers are welcome to contribute in four places:

  • the general measure-theoretic pieces: Gaussian invariance, image laws under partial trace, and the dilation lemma, which are reusable beyond this mission;
  • the deterministic linear algebra of eqs. (12) and (24);
  • Lemma 2 through Borel–Cantelli;
  • any proof of Theorem 2.

Selected references

  • I. Nechita, Asymptotics of random density matrices, Ann. Henri Poincaré 8 (2007); arXiv:quant-ph/0702154v3. https://arxiv.org/abs/quant-ph/0702154v3
  • K. Życzkowski, H.-J. Sommers, Induced measures in the space of mixed quantum states, J. Phys. A 34 (2001) 7111–7125. https://doi.org/10.1088/0305-4470/34/35/335
  • U. Haagerup, S. Thorbjørnsen, Random matrices with complex Gaussian entries, Expo. Math. 21 (2003) 293–335. https://doi.org/10.1016/S0723-0869(03)80036-1
  • V. A. Marchenko, L. A. Pastur, Distribution of eigenvalues for some sets of random matrices, Math. USSR-Sb. 1 (1967) 457–483. https://doi.org/10.1070/SM1967v001n04ABEH001994
  • D. N. Page, Average entropy of a subsystem, Phys. Rev. Lett. 71 (1993) 1291–1294. https://doi.org/10.1103/PhysRevLett.71.1291
12 thms3 active usersReviewed
Mathematical PhysicsQuantum InformationQuantum Mechanics·Captain: mikedeng1

Teleportation Stretching for Single-Mode Gaussian Channels: Exact LOCC Simulation of a Pure-Loss Channel from a Finite-Energy Effective Choi StateResearch Paper

Motivation

The ultimate rates of quantum communication over optical fibre — how many secret bits or entangled pairs per channel use a lossy line can carry with unlimited local operations and classical communication (LOCC) — are bounded through a technique called teleportation stretching (Pirandola, Laurenza, Ottaviani, Banchi, arXiv:1510.08863). The idea is that if a channel E\mathcal EE can be simulated by teleporting its input through a fixed resource state ρE\rho_{\mathcal E}ρE​, then any adaptive protocol using E\mathcal EE nnn times can be rewritten as LOCC acting on ρE⊗n\rho_{\mathcal E}^{\otimes n}ρE⊗n​, and every LOCC monotone of ρE\rho_{\mathcal E}ρE​ bounds the protocol's output.

For the pure-loss channel the natural resource is the Choi state I⊗Eη(ΦEPR)\mathcal I\otimes\mathcal E_\eta(\Phi_{\rm EPR})I⊗Eη​(ΦEPR​) of an ideal Einstein–Podolsky–Rosen state, which is not a normalizable state: it is the limit ξ→1\xi\to1ξ→1 of two-mode squeezed states and has infinite energy. R. Namiki (arXiv:1603.05292, 2016) observed that this limit can be avoided for lossy channels. Finite-squeezing teleportation, in the Braunstein–Kimble scheme (Braunstein and Kimble, PRL 80, 869, 1998), does not teleport perfectly, but its imperfection is itself a pure loss. Composing it with a second pure-loss channel therefore reproduces the target channel exactly, with a finite-energy resource.

Setting

A single bosonic mode is described on the Fock space ℓ2(N)\ell^2(\mathbb N)ℓ2(N) with number basis ∣n⟩|n\rangle∣n⟩. Every operator is handled through its number-basis kernel K(m,n)=⟨m∣X∣n⟩K(m,n)=\langle m|X|n\rangleK(m,n)=⟨m∣X∣n⟩; an operator on two modes has kernel K((r,b),(r′,b′))K((r,b),(r',b'))K((r,b),(r′,b′)), the first index labelling the first mode.

  • The displacement D(z)=eza†−zˉaD(z)=e^{za^\dagger-\bar z a}D(z)=eza†−zˉa, z∈Cz\in\mathbb Cz∈C, has explicit matrix entries ⟨m∣D(z)∣n⟩\langle m|D(z)|n\rangle⟨m∣D(z)∣n⟩; conjugation LD(z)(X)=D(z)XD(z)†\mathcal L_{D(z)}(X)=D(z)XD(z)^\daggerLD(z)​(X)=D(z)XD(z)† acts on kernels by matrix products.
  • The pure-loss channel Eη\mathcal E_\etaEη​, η∈(0,1)\eta\in(0,1)η∈(0,1), is the vacuum beam splitter of transmissivity η\etaη: a↦η aa\mapsto\sqrt\eta\,aa↦η​a with vacuum noise. I⊗Eη\mathcal I\otimes\mathcal E_\etaI⊗Eη​ applies it to the second mode.
  • The two-mode squeezed state with parameter ξ∈(0,1)\xi\in(0,1)ξ∈(0,1) is ∣ψξ⟩=1−ξ2∑n≥0ξn∣n⟩∣n⟩|\psi_\xi\rangle=\sqrt{1-\xi^2}\sum_{n\ge0}\xi^n|n\rangle|n\rangle∣ψξ​⟩=1−ξ2​∑n≥0​ξn∣n⟩∣n⟩ on modes (A′,B)(A',B)(A′,B).
  • The CV-Bell states are ∣Φβ⟩=(1⊗D†(β))∣Φ0⟩|\Phi_\beta\rangle=(\mathbb 1\otimes D^\dagger(\beta))|\Phi_0\rangle∣Φβ​⟩=(1⊗D†(β))∣Φ0​⟩ with ∣Φ0⟩=(2π)−1/2∑n∣n⟩∣n⟩|\Phi_0\rangle=(2\pi)^{-1/2}\sum_n|n\rangle|n\rangle∣Φ0​⟩=(2π)−1/2∑n​∣n⟩∣n⟩; they are improper vectors with ∬∣Φβ⟩⟨Φβ∣ dx dp=1\iint|\Phi_\beta\rangle\langle\Phi_\beta|\,dx\,dp=\mathbb 1∬∣Φβ​⟩⟨Φβ​∣dxdp=1, where β=(x+ip)/2\beta=(x+ip)/\sqrt2β=(x+ip)/2​.

Alice holds a two-mode state ρRS\rho_{RS}ρRS​ (SSS the signal, RRR a reference) and Alice and Bob share a resource ωA′B\omega_{A'B}ωA′B​. The unnormalized branch for outcome β\betaβ is the kernel on (R,B)(R,B)(R,B)

⟨Φβ∣SA′(ρ⊗ω)∣Φβ⟩SA′=12π∑n,n′,k,k′⟨n∣D(β)∣k⟩ ρ((⋅,n),(⋅,n′)) ω((k,⋅),(k′,⋅)) ⟨n′∣D(β)∣k′⟩‾.\langle\Phi_\beta|_{SA'}(\rho\otimes\omega)|\Phi_\beta\rangle_{SA'}=\frac1{2\pi}\sum_{n,n',k,k'}\langle n|D(\beta)|k\rangle\,\rho\big((\cdot,n),(\cdot,n')\big)\,\omega\big((k,\cdot),(k',\cdot)\big)\,\overline{\langle n'|D(\beta)|k'\rangle}.⟨Φβ​∣SA′​(ρ⊗ω)∣Φβ​⟩SA′​=2π1​n,n′,k,k′∑​⟨n∣D(β)∣k⟩ρ((⋅,n),(⋅,n′))ω((k,⋅),(k′,⋅))⟨n′∣D(β)∣k′⟩​.

Formalization targets

Goal: the Theorem (26)–(27), corrected protocol

For 0<η<ξ2<10<\eta<\xi^2<10<η<ξ2<1 put η′=η/ξ2\eta'=\eta/\xi^2η′=η/ξ2 and ρE′=I⊗Eη′(ψξ)\rho_{\mathcal E'}=\mathcal I\otimes\mathcal E_{\eta'}(\psi_\xi)ρE′​=I⊗Eη′​(ψξ​), the effective Choi state. For every two-mode density ρAB\rho_{AB}ρAB​,

I⊗Eη(ρAB)=∬R2L1⊗D(η′ξβˉ)(⟨Φβ∣SA′(ρAB⊗ρE′)∣Φβ⟩SA′) dx dp,\mathcal I\otimes\mathcal E_\eta(\rho_{AB})=\iint_{\mathbb R^2}\mathcal L_{\mathbb 1\otimes D(\sqrt{\eta'}\xi\bar\beta)}\Big(\langle\Phi_\beta|_{SA'}(\rho_{AB}\otimes\rho_{\mathcal E'})|\Phi_\beta\rangle_{SA'}\Big)\,dx\,dp ,I⊗Eη​(ρAB​)=∬R2​L1⊗D(η′​ξβˉ​)​(⟨Φβ​∣SA′​(ρAB​⊗ρE′​)∣Φβ​⟩SA′​)dxdp,

entry by entry, with every series and integral convergent, and η≤η′<1\eta\le\eta'<1η≤η′<1. The parameters and the protocol are independent of ρAB\rho_{AB}ρAB​.

Milestones

  1. (18) Displacement covariance: Eη(D(β)ρD†(β))=D(ηβ)Eη(ρ)D†(ηβ)\mathcal E_\eta(D(\beta)\rho D^\dagger(\beta))=D(\sqrt\eta\beta)\mathcal E_\eta(\rho)D^\dagger(\sqrt\eta\beta)Eη​(D(β)ρD†(β))=D(η​β)Eη​(ρ)D†(η​β) for every density.
  2. Composition (App. B): Eη∘Eη′=Eηη′\mathcal E_\eta\circ\mathcal E_{\eta'}=\mathcal E_{\eta\eta'}Eη​∘Eη′​=Eηη′​.
  3. (20), coherent input, corrected: ⟨Φβ∣(∣α⟩⟨α∣⊗ψξ)∣Φβ⟩=1−ξ22πe−(1−ξ2)∣α−βˉ∣2∣ξ(α−βˉ)⟩⟨ξ(α−βˉ)∣\langle\Phi_\beta|(|\alpha\rangle\langle\alpha|\otimes\psi_\xi)|\Phi_\beta\rangle=\frac{1-\xi^2}{2\pi}e^{-(1-\xi^2)|\alpha-\bar\beta|^2}|\xi(\alpha-\bar\beta)\rangle\langle\xi(\alpha-\bar\beta)|⟨Φβ​∣(∣α⟩⟨α∣⊗ψξ​)∣Φβ​⟩=2π1−ξ2​e−(1−ξ2)∣α−βˉ​∣2∣ξ(α−βˉ​)⟩⟨ξ(α−βˉ​)∣.
  4. (22), coherent input: the weights 1−ξ22πe−(1−ξ2)∣α−βˉ∣2\frac{1-\xi^2}{2\pi}e^{-(1-\xi^2)|\alpha-\bar\beta|^2}2π1−ξ2​e−(1−ξ2)∣α−βˉ​∣2 integrate to 111 over dx dpdx\,dpdxdp.
  5. (23), coherent input, corrected: after Eη\mathcal E_\etaEη​ the branch of milestone 3 becomes the same weight times ∣ηξ(α−βˉ)⟩⟨ηξ(α−βˉ)∣|\sqrt\eta\xi(\alpha-\bar\beta)\rangle\langle\sqrt\eta\xi(\alpha-\bar\beta)|∣η​ξ(α−βˉ​)⟩⟨η​ξ(α−βˉ​)∣.
  6. (24), every density, corrected: ∬D(ηξβˉ) Eη(Tβ,ξρTβ,ξ†) D†(ηξβˉ) dx dp=Eηξ2(ρ)\iint D(\sqrt\eta\xi\bar\beta)\,\mathcal E_\eta(T_{\beta,\xi}\rho T_{\beta,\xi}^\dagger)\,D^\dagger(\sqrt\eta\xi\bar\beta)\,dx\,dp=\mathcal E_{\eta\xi^2}(\rho)∬D(η​ξβˉ​)Eη​(Tβ,ξ​ρTβ,ξ†​)D†(η​ξβˉ​)dxdp=Eηξ2​(ρ).

Significance

The result. The Theorem shows that one use of a pure-loss channel, on any input including half of an entangled state, is reproduced exactly by LOCC on the finite-energy state I⊗Eη′(ψξ)\mathcal I\otimes\mathcal E_{\eta'}(\psi_\xi)I⊗Eη′​(ψξ​). In the paper's §II.C this is the step that would let entanglement-measure bounds for nnn adaptive channel uses be evaluated on a genuine density operator, without the limit ξ→1\xi\to1ξ→1 that the ideal-EPR argument needs. The relation (24) also makes precise in which sense the realistic teleportation family {Tβ,ξ}\{T_{\beta,\xi}\}{Tβ,ξ​} is not teleportation-stretching in the finite-dimensional sense, yet simulates the channel after an outcome-dependent displacement.

Formalizing it. The result is proved in the paper by a short computation through the Glauber–Sudarshan PPP representation, which is heuristic for nonclassical inputs. No machine-checked version exists. A formal proof requires an argument valid for every density operator, together with all convergence statements. The formalization also records a sign slip: with the paper's own definitions (4), (16), (A4) and D(β)=eβa†−β∗aD(\beta)=e^{\beta a^\dagger-\beta^*a}D(β)=eβa†−β∗a, the step ⟨α∗∣D(β)=⟨α∗+β∗∣\langle\alpha^*|D(\beta)=\langle\alpha^*+\beta^*|⟨α∗∣D(β)=⟨α∗+β∗∣ in (20) should read ⟨α∗−β∣\langle\alpha^*-\beta|⟨α∗−β∣. Consequently (20) and (23) hold with α−βˉ\alpha-\bar\betaα−βˉ​ in place of α+β\alpha+\betaα+β, and the correction in (24) is D(ηξβˉ)D(\sqrt\eta\xi\bar\beta)D(η​ξβˉ​) rather than D†(ηξβ)D^\dagger(\sqrt\eta\xi\beta)D†(η​ξβ); a numerical evaluation of the printed (24) for the input 0.6∣0⟩+0.8i∣1⟩0.6|0\rangle+0.8i|1\rangle0.6∣0⟩+0.8i∣1⟩ gives an error of 0.380.380.38 in the output matrix, against 8×10−58\times10^{-5}8×10−5 (grid-limited) for the corrected one. The Theorem itself, existential in the protocol, is unaffected.

Difficulty

The obvious route is the paper's: write ρ=∫Pα∣α⟩⟨α∣ d2α\rho=\int P_\alpha|\alpha\rangle\langle\alpha|\,d^2\alphaρ=∫Pα​∣α⟩⟨α∣d2α, compute everything for coherent states, and integrate. For nonclassical states PPP is not a function, or even a measure, so this route is not a proof; passing from coherent inputs to all densities needs a density or continuity argument in trace norm, with interchange of the outcome integral and the infinite Fock series. The Bell projection itself is a contraction against improper vectors, here encoded as an absolutely convergent four-fold series whose convergence for arbitrary trace-class inputs is part of the work. Finally, the composition Eη∘Eη′=Eηη′\mathcal E_\eta\circ\mathcal E_{\eta'}=\mathcal E_{\eta\eta'}Eη​∘Eη′​=Eηη′​ and the covariance (18) are standard on covariance matrices but must be established on number-basis kernels, where they are identities between infinite series with binomial weights.

Formalization scope

  • Representation. All states and outputs are number-basis kernels. Inputs are bounded operators on ℓ2(N)\ell^2(\mathbb N)ℓ2(N) or ℓ2(N×N)\ell^2(\mathbb N\times\mathbb N)ℓ2(N×N) satisfying the published density predicates (trace class, self-adjoint, positive, unit trace), from QAlgorithms_Papers_Independent_Dependency50_P39; the loss channel is the vacuum-loss kernel of QAlgorithms_Papers_Independent_Frontier30_P21; coherent states use its coherentDensity.
  • Bell projection. It is the Fock form of (16) with (A4), with the factor (2π)−1(2\pi)^{-1}(2π)−1 explicit; the CV-Bell states are not formalized as vectors. The outcome is β=(x+ip)/2\beta=(x+ip)/\sqrt2β=(x+ip)/2​ and the average is over R×R\mathbb R\times\mathbb RR×R with Lebesgue measure, the paper's dx dpdx\,dpdxdp.
  • Conventions. The resource has Alice's mode A′A'A′ first and Bob's mode BBB second; the loss of ρE′\rho_{\mathcal E'}ρE′​ acts on BBB. Every series is asserted summable and every integrand integrable as part of the conclusion, so default values of divergent series or non-integrable functions never satisfy a statement.
  • Corrected sign. Milestones (20), (23), (24) and the goal use α−βˉ\alpha-\bar\betaα−βˉ​ and the correction D(η′ξβˉ)D(\sqrt{\eta'}\xi\bar\beta)D(η′​ξβˉ​), as explained above.
  • Coherent-input restriction. Milestones (20), (22), (23) are stated for coherent inputs (PPP a point mass), because the PPP representation (19) is not available for general states; (24) and the goal are stated for every density, as printed.
  • LOCC. The general class of LOCC maps is not formalized; Λ\LambdaΛ is the explicit protocol of (24). An existential "there is a map Λ\LambdaΛ with Λ(ρE′)=I⊗Eη(ρAB)\Lambda(\rho_{\mathcal E'})=\mathcal I\otimes\mathcal E_\eta(\rho_{AB})Λ(ρE′​)=I⊗Eη​(ρAB​)" would be trivially true (take Λ\LambdaΛ constant) and is ruled out by stating the protocol, with η′\eta'η′, ξ\xiξ and the correction fixed before ρAB\rho_{AB}ρAB​; likewise the Bell branch is the defining series, never its closed form.
  • Not covered. The thermal-loss and amplifier versions (§III, (30)–(33)), the nnn-use relative-entropy bound (§II.C), and the finite-dimensional review (§II.A). The statement Eη(∣α⟩⟨α∣)=∣ηα⟩⟨ηα∣\mathcal E_\eta(|\alpha\rangle\langle\alpha|)=|\sqrt\eta\alpha\rangle\langle\sqrt\eta\alpha|Eη​(∣α⟩⟨α∣)=∣η​α⟩⟨η​α∣ used inside (20) is the existing Open goal Frontier30.P21.coherentOutput and is not posed again.
  • Reusable parts. Proofs of displacement covariance and of channel composition on Fock kernels, and summability lemmas for displacement matrices against trace-class kernels, are useful beyond this mission; contributions of such lemmas are welcome.

Selected references

  • R. Namiki, Teleportation stretching for single-mode Gaussian channels, arXiv:1603.05292v1, 2016. https://arxiv.org/abs/1603.05292
  • S. Pirandola, R. Laurenza, C. Ottaviani, L. Banchi, Fundamental limits of repeaterless quantum communications, arXiv:1510.08863v3, 2015. https://arxiv.org/abs/1510.08863
  • S. L. Braunstein, H. J. Kimble, Teleportation of continuous quantum variables, Phys. Rev. Lett. 80, 869, 1998. https://doi.org/10.1103/PhysRevLett.80.869
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Mathematical PhysicsQuantum Information·Captain: mikedeng1

Preparation of Entangled States by Quantum Markov Processes: Every n-Qubit Pure State Is the Unique and Attractive Stationary State of a Dissipative Process with Relaxation Time 1/min_k g_kResearch Paper

Motivation

Quantum technologies usually prepare a desired state by isolating a system and driving it with carefully timed unitary operations; coupling to an environment is treated as noise. Dissipative state preparation reverses this view: one engineers the system–environment coupling so that the open-system dynamics itself drives every initial state into a prescribed target state. Because the target is a fixed point that attracts all initial conditions, the preparation needs no initialization and is self-correcting against perturbations that leave the dynamics unchanged.

In 2008 three closely related papers made this programme concrete. Diehl, Micheli, Kantian, Kraus, Büchler and Zoller showed how cold atoms can be driven into condensed many-body states by engineered dissipation (arXiv:0803.1482); Verstraete, Wolf and Cirac showed that dissipative dynamics can perform universal quantum computation and prepare matrix product and PEPS states (arXiv:0803.1447); and Kraus, Büchler, Diehl, Kantian, Micheli and Zoller, Preparation of entangled states by quantum Markov processes (arXiv:0803.1463), gave the general uniqueness criteria and the explicit construction this mission formalizes. The last paper's central claim is that every pure state of nnn qubits is the unique stationary state of an explicitly given Markovian process, and that this process relaxes on a time scale independent of nnn.

Setting

A quantum Markov process on a ddd-dimensional Hilbert space is a master equation ρ˙=L(ρ)\dot\rho = \mathcal L(\rho)ρ˙​=L(ρ) for a density matrix ρ\rhoρ (a positive semidefinite d×dd\times dd×d complex matrix of trace one). In the paper's Eq. (2) the generator, or Liouvillian, is

LH,cl(ρ)=−i[H,ρ]+∑lgl(2clρcl†−cl†clρ−ρcl†cl),\mathcal L_{H,c_l}(\rho) = -i[H,\rho] + \sum_l g_l\bigl(2c_l\rho c_l^\dagger - c_l^\dagger c_l\rho - \rho c_l^\dagger c_l\bigr),LH,cl​​(ρ)=−i[H,ρ]+l∑​gl​(2cl​ρcl†​−cl†​cl​ρ−ρcl†​cl​),

where HHH is a Hermitian matrix (the Hamiltonian), the clc_lcl​ are arbitrary complex matrices (the jump operators) and glg_lgl​ are rates. The solution is ρ(t)=etL(ρ(0))\rho(t) = e^{t\mathcal L}(\rho(0))ρ(t)=etL(ρ(0)). The paper writes P=∑lglcl†clP = \sum_l g_l c_l^\dagger c_lP=∑l​gl​cl†​cl​, Q=P−iHQ = P - iHQ=P−iH and E(ρ)=2∑lglclρcl†\mathcal E(\rho) = 2\sum_l g_l c_l\rho c_l^\daggerE(ρ)=2∑l​gl​cl​ρcl†​.

A stationary state is a density matrix with L(ρ)=0\mathcal L(\rho) = 0L(ρ)=0. A vector annihilated by every clc_lcl​ and an eigenvector of HHH is a dark vector; the set D=K(P)∩ES(H)D = K(P)\cap ES(H)D=K(P)∩ES(H) of dark vectors, when it is a subspace (all dark vectors share one eigenvalue EEE of HHH), yields the dark states, the density matrices with range inside DDD.

For nnn qubits, σ−(k)=1⊗⋯⊗∣0⟩k⟨1∣⊗⋯⊗1\sigma_-^{(k)} = \mathbb 1\otimes\dots\otimes|0\rangle_k\langle1|\otimes\dots\otimes\mathbb 1σ−(k)​=1⊗⋯⊗∣0⟩k​⟨1∣⊗⋯⊗1 lowers qubit kkk from ∣1⟩|1\rangle∣1⟩ to ∣0⟩|0\rangle∣0⟩. Given any unitary UUU, the target is ∣Ψ⟩=U∣0n⟩|\Psi\rangle = U|0^n\rangle∣Ψ⟩=U∣0n⟩, which ranges over all pure states, and the construction of §IV uses the nnn jump operators ck=Uσ−(k)U†c_k = U\sigma_-^{(k)}U^\daggerck​=Uσ−(k)​U† with rates gkg_kgk​. The relaxation time TrelaxT_{\mathrm{relax}}Trelax​ is the inverse of the least value of −Re⁡μ-\operatorname{Re}\mu−Reμ over the nonzero eigenvalues μ\muμ of L\mathcal LL.

Formalization targets

Goal: unique, attractive, and fast

For n≥1n\ge1n≥1, any unitary UUU and rates g1,…,gn>0g_1,\dots,g_n>0g1​,…,gn​>0:

(a)∀H=H† with H∣Ψ⟩=E∣Ψ⟩:LH,ck(ρ)=0, ρ a density matrix  ⟺  ρ=∣Ψ⟩⟨Ψ∣;\text{(a)}\quad \forall H = H^\dagger \text{ with } H|\Psi\rangle = E|\Psi\rangle:\qquad \mathcal L_{H,c_k}(\rho) = 0,\ \rho \text{ a density matrix} \iff \rho = |\Psi\rangle\langle\Psi|;(a)∀H=H† with H∣Ψ⟩=E∣Ψ⟩:LH,ck​​(ρ)=0, ρ a density matrix⟺ρ=∣Ψ⟩⟨Ψ∣; (b)etL{ck}(ρ)→t→∞∣Ψ⟩⟨Ψ∣ for every density matrix ρ;(c)Trelax=1min⁡kgk.\text{(b)}\quad e^{t\mathcal L_{\{c_k\}}}(\rho)\xrightarrow[t\to\infty]{}|\Psi\rangle\langle\Psi| \text{ for every density matrix } \rho;\qquad \text{(c)}\quad T_{\mathrm{relax}} = \frac{1}{\min_k g_k}.(b)etL{ck​}​(ρ)t→∞​∣Ψ⟩⟨Ψ∣ for every density matrix ρ;(c)Trelax​=mink​gk​1​.

Clause (a) allows every Hamiltonian that has the target as an eigenvector; (b) and (c) concern the purely dissipative process H=0H = 0H=0, the one the paper solves explicitly.

Milestones

  1. Dark states are stationary (§II, p. 2).
  2. Theorem 1 (p. 2): L(∣Φ⟩⟨Φ∣)=0\mathcal L(|\Phi\rangle\langle\Phi|) = 0L(∣Φ⟩⟨Φ∣)=0 iff Q†∣Φ⟩=λ∣Φ⟩Q^\dagger|\Phi\rangle = \lambda|\Phi\rangleQ†∣Φ⟩=λ∣Φ⟩ and cl∣Φ⟩=λl∣Φ⟩c_l|\Phi\rangle = \lambda_l|\Phi\ranglecl​∣Φ⟩=λl​∣Φ⟩ with ∑lgl∣λl∣2=Re⁡λ\sum_l g_l|\lambda_l|^2 = \operatorname{Re}\lambda∑l​gl​∣λl​∣2=Reλ.
  3. D′=DD' = DD′=D (p. 3, footnote [35]): Q†ψ=iλψQ^\dagger\psi = i\lambda\psiQ†ψ=iλψ with λ\lambdaλ real iff ψ\psiψ is dark.
  4. Theorem 2 (p. 3): if no nonzero subspace S⊥DS\perp DS⊥D satisfies clS⊆Sc_lS\subseteq Scl​S⊆S for all lll, the only stationary states are the dark states.
  5. Dark vectors of {ck}\{c_k\}{ck​} (§IV, p. 4): ckφ=0c_k\varphi = 0ck​φ=0 for all kkk iff φ∈C∣Ψ⟩\varphi\in\mathbb C|\Psi\rangleφ∈C∣Ψ⟩.
  6. The monomial claim (§IV, p. 4): for χ⊥∣0n⟩\chi\perp|0^n\rangleχ⊥∣0n⟩, χ≠0\chi\ne0χ=0, some product PPP of the σ−(k)\sigma_-^{(k)}σ−(k)​ has ⟨0n∣P∣χ⟩≠0\langle0^n|P|\chi\rangle\ne0⟨0n∣P∣χ⟩=0.
  7. Unitary covariance (§IV.A, p. 4): L{Uσ−(k)U†}(σ)=UL{σ−(k)}(U†σU)U†\mathcal L_{\{U\sigma_-^{(k)}U^\dagger\}}(\sigma) = U\mathcal L_{\{\sigma_-^{(k)}\}}(U^\dagger\sigma U)U^\daggerL{Uσ−(k)​U†}​(σ)=UL{σ−(k)​}​(U†σU)U†.
  8. One qubit (§IV.A, p. 4): eigenvalues 0,−2g,−g,−g0, -2g, -g, -g0,−2g,−g,−g on ∣0⟩⟨0∣,σz,σx,σy|0\rangle\langle0|, \sigma_z, \sigma_x, \sigma_y∣0⟩⟨0∣,σz​,σx​,σy​.
  9. Product spectrum (§IV.A, p. 4): the explicit operators U(σi1(1)⊗⋯⊗σin(n))U†U(\sigma_{i_1}^{(1)}\otimes\cdots\otimes\sigma_{i_n}^{(n)})U^\daggerU(σi1​(1)​⊗⋯⊗σin​(n)​)U† form an eigenbasis, with eigenvalues ∑kλik\sum_k\lambda_{i_k}∑k​λik​​; these are exactly −∑kmkgk-\sum_k m_kg_k−∑k​mk​gk​, mk∈{0,1,2}m_k\in\{0,1,2\}mk​∈{0,1,2}.

Significance

The result. The goal shows that dissipative preparation is universal for pure states: no pure state, however entangled, is out of reach, and the required jump operators are written down explicitly. Clause (c) states that the time to reach the target is governed by the weakest single coupling and does not grow with the number of qubits. Theorems 1 and 2 are general tools independent of the construction: Theorem 1 characterizes every pure stationary state of every Lindblad generator, and Theorem 2 is a checkable sufficient condition for the absence of other stationary states. The later sections of the paper (stabilizer and graph states, PEPS and the AKLT state, a driven condensate, the η\etaη-condensate) apply these tools to physically local processes.

Formalizing it. The results are classical and proved in the paper, partly in sketched form ("straightforward", "the same arguments as above"). No machine-checked proof of any of them is known. The formalization supplies complete arguments for the sketched steps, fixes the guards the printed statements omit (listed below), and produces a reusable layer of statements about the spectrum and stationary states of Lindblad generators on finite-dimensional spaces.

Difficulty

The first idea for uniqueness is to show that the target is the only pure stationary state, using Theorem 1. That is not enough: a Lindblad generator can have mixed stationary states even when its pure stationary state is unique (with Hermitian jump operators the maximally mixed state is always stationary, Eq. (4)). Excluding mixed stationary states is the content of Theorem 2, whose proof requires a positivity argument for Q†ρ+ρQQ^\dagger\rho + \rho QQ†ρ+ρQ and a decomposition of ρ\rhoρ along D⊕D⊥D\oplus D^\perpD⊕D⊥.

For convergence, uniqueness of the stationary state does not suffice either: a purely imaginary eigenvalue would make etL(ρ)e^{t\mathcal L}(\rho)etL(ρ) oscillate forever. Clauses (b) and (c) need the full spectrum of the 4n4^n4n-dimensional Liouvillian, obtained by reducing to U=1U = \mathbb 1U=1 and decoupling into nnn single-qubit problems; in Lean this means relating the eigenvalues of a generator on 2n×2n2^n\times 2^n2n×2n matrices to those of tensor factors.

Formalization scope

The generator, the semigroup etLe^{t\mathcal L}etL (matrix exponential of the Liouvillian as a linear map), and the predicates stationary (density matrix with Lρ=0\mathcal L\rho = 0Lρ=0), attractive and lazy (nonzero subspace invariant under all jump operators) are reused from the published definition QAlgorithms_Papers_Independent_Dependency50_P16. That layer normalizes the dissipator as ∑kVkρVk†−12{Vk†Vk,ρ}\sum_k V_k\rho V_k^\dagger - \frac12\{V_k^\dagger V_k,\rho\}∑k​Vk​ρVk†​−21​{Vk†​Vk​,ρ}; the paper's rates enter through jump operators Vl=2gl clV_l = \sqrt{2g_l}\,c_lVl​=2gl​​cl​, which reproduces Eq. (2) exactly. Operators are complex d×dd\times dd×d matrices, states are vectors in Cd\mathbb C^dCd, and nnn qubits are indexed by {0,…,2n−1}\{0,\dots,2^n-1\}{0,…,2n−1} with qubit 1 the most significant bit; ∣0n⟩|0^n\rangle∣0n⟩ is the basis vector of index 000. Density matrices are positive semidefinite of trace one, and ∣ψ⟩⟨ψ∣|\psi\rangle\langle\psi|∣ψ⟩⟨ψ∣ is formed without normalization, so unit norm is assumed where the paper says "state". Convergence is entrywise.

Conventions added to the paper and disclosed in each statement:

  • rates gl>0g_l>0gl​>0 (the paper allows gℓ≥0g_\ell\ge0gℓ​≥0; with a zero rate Theorem 1 and uniqueness fail);
  • Theorem 2 quantifies over nonzero subspaces (the zero subspace would make its hypothesis unsatisfiable), and keeps the standing assumption that DDD is a subspace, as a fixed eigenvalue EEE;
  • n≥1n\ge1n≥1 in clause (c); the relaxation rate is a least element and must be positive before its reciprocal is a finite relaxation time;
  • "∣φ⟩=U∣0n⟩|\varphi\rangle = U|0^n\rangle∣φ⟩=U∣0n⟩" is read up to a scalar, and the single-qubit "iff σi∈Σk\sigma_i\in\Sigma_kσi​∈Σk​" as four eigen-equations.

A trivializing formalization is ruled out: the target is not fixed to U=1U = \mathbb 1U=1, the jump operators are the conjugated Uσ−(k)U†U\sigma_-^{(k)}U^\daggerUσ−(k)​U† and not σ−(k)\sigma_-^{(k)}σ−(k)​, clause (a) quantifies over all Hamiltonians with the target as eigenvector rather than only H=0H = 0H=0, and the relaxation time refers to the actual nonzero spectrum of the generator.

A complete development needs: properties of the Lindblad generator under unitary conjugation; positivity arguments for completely positive maps on matrices; the tensor-product structure of 2n×2n2^n\times 2^n2n×2n matrices over qubit wires; and the spectrum and diagonalizability of a sum of commuting single-site generators. The spectral and stationary-state lemmas are reusable for any finite-dimensional open-system mission. Contributions to any milestone, and alternative proofs of Theorem 2 or of the spectrum, are welcome.

Selected references

  • B. Kraus, H. P. Büchler, S. Diehl, A. Kantian, A. Micheli, P. Zoller, Preparation of entangled states by quantum Markov processes, Phys. Rev. A 78 (2008); arXiv:0803.1463v3. https://arxiv.org/abs/0803.1463
  • S. Diehl, A. Micheli, A. Kantian, B. Kraus, H. P. Büchler, P. Zoller, Quantum states and phases in driven open quantum systems with cold atoms, Nature Physics 4 (2008). https://arxiv.org/abs/0803.1482
  • F. Verstraete, M. M. Wolf, J. I. Cirac, Quantum computation and quantum-state engineering driven by dissipation, Nature Physics 5 (2009). https://arxiv.org/abs/0803.1447
  • E. Malvetti et al., Reachability, coolability, and stabilizability of open Markovian quantum systems with fast unitary control, 2023; arXiv:2308.00561. https://arxiv.org/abs/2308.00561
  • G. Lindblad, On the generators of quantum dynamical semigroups, Comm. Math. Phys. 48 (1976). https://doi.org/10.1007/BF01608499
  • V. Gorini, A. Kossakowski, E. C. G. Sudarshan, Completely positive dynamical semigroups of N-level systems, J. Math. Phys. 17 (1976). https://doi.org/10.1063/1.522979
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Dynamical SystemsMathematical PhysicsProbability+1·Captain: mikedeng1

Entropic Fluctuations in Statistical Mechanics I. Classical Dynamical Systems: Finite-Time Green–Kubo Formula and Onsager Reciprocity from the Generalized Evans–Searles SymmetryResearch Paper

Motivation

In a system driven away from equilibrium, a force changes a reference state and a dynamics, while a corresponding flux records the transported quantity. Statistical mechanics asks how the mean flux changes under a small force. A second question is whether the matrix of these changes is symmetric when the underlying dynamics admit time reversal. Jakšić, Pillet and Rey-Bellet derive both answers at a fixed, finite observation time from a generalized Evans–Searles fluctuation symmetry for classical dynamical systems in their 2010 preprint, §4. This finite-time statement does not require a large-time limit, a stationary nonequilibrium state, or an asymptotic fluctuation theorem.

The paper first develops the entropy cocycle and its time-reversal properties for a single system. Its Proposition 3.3 gives a scalar finite-time Evans–Searles identity. Section 4 lets the dynamics and reference state depend on a vector of forces and introduces a vector of fluxes. Proposition 4.6, the target here, is the section’s stated finite-time Green–Kubo and Onsager result Jakšić, Pillet and Rey-Bellet, pp. 18–21.

Setting

Fix a positive integer NNN and a measurable configuration space MMM. Each force vector X∈RNX\in\mathbb R^NX∈RN determines a continuous-time measurable flow ϕXs:M→M\phi_X^s:M\to MϕXs​:M→M, a probability state ωX\omega_XωX​, and an entropy-production observable σX\sigma_XσX​. The evolved states of each system are equivalent to ωX\omega_XωX​, and the regularity assumption (E1) makes σX\sigma_XσX​ the derivative of the evolved-state density in L1(ωX)L^1(\omega_X)L1(ωX​). The family also has a measurable flux vector ΦX=(ΦX(1),…,ΦX(N))\Phi_X=(\Phi_X^{(1)},\ldots,\Phi_X^{(N)})ΦX​=(ΦX(1)​,…,ΦX(N)​) satisfying the flux relation σX=X⋅ΦX\sigma_X=X\cdot\Phi_XσX​=X⋅ΦX​ almost everywhere. These are the family and flux conventions of §4.1 Jakšić, Pillet and Rey-Bellet, pp. 17–18.

At equilibrium, X=0X=0X=0. Assumption (T1) says only that ω0\omega_0ω0​ is invariant under ϕ0s\phi_0^sϕ0s​; no stationarity is imposed on ωX\omega_XωX​ for nonzero XXX. Assumption (T2) gives each XXX-system a possibly different time reversal ϑX\vartheta_XϑX​ preserving ωX\omega_XωX​, reversing the flow, and sending ΦX\Phi_XΦX​ to −ΦX-\Phi_X−ΦX​ almost everywhere. For t>0t>0t>0, the XXX-system time average is

⟨ΦX(j)⟩t=1t∫0tωX(ΦX(j)∘ϕXs) ds,\langle\Phi_X^{(j)}\rangle_t=\frac1t\int_0^t\omega_X(\Phi_X^{(j)}\circ\phi_X^s)\,ds,⟨ΦX(j)​⟩t​=t1​∫0t​ωX​(ΦX(j)​∘ϕXs​)ds,

and its force derivative at equilibrium is Ljkt=∂Xk⟨ΦX(j)⟩t∣X=0L_{jkt}=\left.\partial_{X_k}\langle\Phi_X^{(j)}\rangle_t\right|_{X=0}Ljkt​=∂Xk​​⟨ΦX(j)​⟩t​​X=0​. The equilibrium correlation with indices j,kj,kj,k is Cjk(s)=ω0(Φ0(k)(Φ0(j)∘ϕ0s))C_{jk}(s)=\omega_0(\Phi_0^{(k)}(\Phi_0^{(j)}\circ\phi_0^s))Cjk​(s)=ω0​(Φ0(k)​(Φ0(j)​∘ϕ0s​)). Thus the time-shifted factor carries index jjj.

The generalized Evans–Searles functional is the log-Laplace transform of the actual integrated flux,

gt(X,Y)=log⁡ωX ⁣(exp⁡[−Y⋅∫0tΦX∘ϕXs ds]),g_t(X,Y)=\log\omega_X\!\left(\exp\left[-Y\cdot\int_0^t\Phi_X\circ\phi_X^s\,ds\right]\right),gt​(X,Y)=logωX​(exp[−Y⋅∫0t​ΦX​∘ϕXs​ds]),

with extended real values when the exponential moment diverges. Proposition 4.3 states gt(X,Y)=gt(X,X−Y)g_t(X,Y)=g_t(X,X-Y)gt​(X,Y)=gt​(X,X−Y) under (T1)–(T2) Jakšić, Pillet and Rey-Bellet, p. 19.

Formalization targets

Generalized finite-time symmetry

Proposition 4.2 relates the law PXtP_X^tPXt​ of the averaged flux to its reflection PˉXt\bar P_X^tPˉXt​ for nonzero ttt: dPˉXt/dPXt(s)=e−tX⋅sd\bar P_X^t/dP_X^t(s)=e^{-tX\cdot s}dPˉXt​/dPXt​(s)=e−tX⋅s. Proposition 4.3 states the equivalent symmetry of gtg_tgt​. Lemma 4.4 is a calculus statement about a C1,2C^{1,2}C1,2 function with that symmetry. These are the three new numbered milestones; the earlier entropy-cocycle results, Propositions 3.1, 3.2 and 3.8, are reused as published references Jakšić, Pillet and Rey-Bellet, pp. 8–12, 19.

Finite-time response

The goal is all of Proposition 4.6, at each fixed t>0t>0t>0:

X↦⟨ΦX⟩t is differentiable at 0,Ljkt=12∫−ttCjk(s)(1−∣s∣t)ds,Ljkt=Lkjt.X\mapsto\langle\Phi_X\rangle_t\text{ is differentiable at }0,\qquad L_{jkt}=\frac12\int_{-t}^{t}C_{jk}(s)\left(1-\frac{|s|}{t}\right)ds,\qquad L_{jkt}=L_{kjt}.X↦⟨ΦX​⟩t​ is differentiable at 0,Ljkt​=21​∫−tt​Cjk​(s)(1−t∣s∣​)ds,Ljkt​=Lkjt​.

The first clause is the paper’s assumption (T3) as a conclusion. The second is the finite-time Green–Kubo formula and the third the finite-time Onsager reciprocity relation Jakšić, Pillet and Rey-Bellet, pp. 20–21.

Significance

The formula determines the first-order response matrix from correlations measured entirely in the equilibrium system. Its triangular weight records a finite observation window. Reciprocity then says that cross-responses agree after exchanging the flux and force coordinates. Both statements are exact at fixed time, so they can be used before an asymptotic transport coefficient is known to exist Jakšić, Pillet and Rey-Bellet, Proposition 4.6 and Remarks 1–2, pp. 20–21.

The paper proves these claims. The formalization task is to express and eventually verify the known result, including the dependent family of states and flows, the extended log moment, and all three clauses of the proposition. The published §3 definitions and cocycle statements provide a reusable base. A checked §4 development would also expose general measure and calculus lemmas for finite-time fluctuation relations. At drafting, these new statements compile as open Lean theorems; compilation does not supply their proofs.

Difficulty

The symmetry is an equality of potentially infinite log moments, while the response matrix uses real derivatives near the origin. Local finiteness must justify passage between those two regimes. The paper’s C1,2C^{1,2}C1,2 convention includes five families of continuous partial derivatives, including both orders of mixed differentiation, but does not demand two XXX derivatives. Treating it as ordinary C2C^2C2 would change the theorem.

There is also a measure-theoretic gap between a time integral of fluxes inside an expectation and an integral of expectations over time. A formal statement must control that interchange and the two-time correlation integral. Stationarity is available only at equilibrium, although both the state and flow vary with XXX in the response derivative. The one-line formula for ⟨f⟩t\langle f\rangle_t⟨f⟩t​ on p. 20 leaves its state implicit; the first display of the proof on p. 21 and the entropy balance used in (4.30) identify it as the XXX-system state for f=ΦXf=\Phi_Xf=ΦX​ Jakšić, Pillet and Rey-Bellet, pp. 20–21.

Formalization scope

The Lean family uses RN\mathbb R^NRN as Fin N → ℝ with N>0N>0N>0, a measurable type MMM, and one published RegularSystem M for each XXX. That type supplies (F1), (F2), (C), and (E1); the new family supplies the flux relation and almost-everywhere time integrability of each coordinate on finite intervals, so the paper's integrated flux is defined. Observable identities in the flux relation and time reversal are ωX\omega_XωX​-almost everywhere. The law PXtP_X^tPXt​ is the pushforward of ωX\omega_XωX​ under the integrated flux divided by ttt. The log-Laplace transform uses a nonnegative integral and EReal, retaining +∞+\infty+∞; a real conversion occurs only on a neighborhood where finiteness is stated.

The goal requires t>0t>0t>0, since its triangular kernel and time average refer to [0,t][0,t][0,t]. Proposition 4.2 requires t≠0t\ne0t=0 because PXtP_X^tPXt​ otherwise contains a division by zero; Proposition 4.3 includes t=0t=0t=0. To make the correlation and Fubini steps meaningful, equilibrium flux coordinates are assumed in L2(ω0)L^2(\omega_0)L2(ω0​) and the XXX-system flux is locally integrable on [0,t]×M[0,t]\times M[0,t]×M near X=0X=0X=0. These are labeled analytic hypotheses, not new physical symmetry assumptions. Coordinate partials are derivatives along coordinate lines, with existence as well as continuity asserted for the five families on an open neighborhood.

The target uses the actual log-Laplace transform and the force derivative of the actual time average. It imposes stationarity only at X=0X=0X=0, uses equilibrium correlations, and keeps precisely C1,2C^{1,2}C1,2 regularity. The family interface and the calculus and integration lemmas needed to prove the milestones are reusable; contributions that close any of the numbered milestones or the analytic interchange obligations fit this scope.

Selected references

  • V. Jakšić, C.-A. Pillet and L. Rey-Bellet, Entropic Fluctuations in Statistical Mechanics I. Classical Dynamical Systems, arXiv preprint arXiv:1009.3248v2, 2010. Preprint.
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Harmonic AnalysisRandom Matrix TheoryRepresentation Theory·Captain: mikedeng1

The Weingarten Calculus: Haar Integrals of Matrix Elements Are Given by a Basis of Invariant Tensors and Its Weingarten Matrix, I = A W A*Research Paper

Motivation

Integrals of polynomial functions of the matrix entries of a random element of a compact group appear across mathematical physics and probability: in random matrix theory (moments of Haar-distributed unitary, orthogonal and symplectic matrices), in lattice gauge theory (the link integrals of U(N)U(N)U(N) gauge fields), and in quantum information (averages of quantum channels over random unitaries, twirling, unitary designs). Weingarten first computed asymptotics of such integrals for U(N)U(N)U(N) in 1978 (Weingarten, J. Math. Phys. 19 (1978)); Collins (IMRN 2003) and Collins–Śniady (CMP 2006) gave exact formulas for the unitary, orthogonal and symplectic groups in terms of what is now called the Weingarten function.

The survey of Collins, Matsumoto and Novak, The Weingarten Calculus (arXiv:2109.14890), isolates the single statement underneath all these formulas, valid for every compact group and every finite-dimensional unitary representation. This mission formalizes that statement, Theorem 2.1 of the survey, together with the steps of its proof as written in Section 2.

Setting

Let GGG be a compact Hausdorff topological group and μ\muμ its Haar probability measure, the unique Borel probability measure on GGG invariant under left translation. Let H=CNH=\mathbb{C}^NH=CN with its standard orthonormal basis e1,…,eNe_1,\dots,e_Ne1​,…,eN​, and let U:G→U(N)U:G\to\mathrm U(N)U:G→U(N) be a continuous group homomorphism into the unitary N×NN\times NN×N matrices. Its matrix elements are Uxy(g)=⟨ex,U(g)ey⟩U_{xy}(g)=\langle e_x,U(g)e_y\rangleUxy​(g)=⟨ex​,U(g)ey​⟩, the (x,y)(x,y)(x,y) entry of U(g)U(g)U(g).

Fix d∈Nd\in\mathbb Nd∈N. A multi-index is a function i:[d]→[N]i:[d]\to[N]i:[d]→[N]; the set of them is Fun(d,N)\mathrm{Fun}(d,N)Fun(d,N). The Weingarten integrals of degree ddd are

Iij=∫G∏x=1dUi(x)j(x)(g) dμ(g),i,j∈Fun(d,N).I_{ij}=\int_G\prod_{x=1}^d U_{i(x)j(x)}(g)\,d\mu(g),\qquad i,j\in\mathrm{Fun}(d,N).Iij​=∫G​x=1∏d​Ui(x)j(x)​(g)dμ(g),i,j∈Fun(d,N).

The tensor power H⊗dH^{\otimes d}H⊗d has the orthonormal basis ei=ei(1)⊗⋯⊗ei(d)e_i=e_{i(1)}\otimes\cdots\otimes e_{i(d)}ei​=ei(1)​⊗⋯⊗ei(d)​, and the tensor-power matrix U⊗d(g)U^{\otimes d}(g)U⊗d(g) has entries Uij⊗d(g)=∏x=1dUi(x)j(x)(g)U^{\otimes d}_{ij}(g)=\prod_{x=1}^dU_{i(x)j(x)}(g)Uij⊗d​(g)=∏x=1d​Ui(x)j(x)​(g). The invariant tensors are

(H⊗d)G={t∈H⊗d:U⊗d(g) t=t for all g∈G},(H^{\otimes d})^G=\{t\in H^{\otimes d}: U^{\otimes d}(g)\,t=t\ \text{for all }g\in G\},(H⊗d)G={t∈H⊗d:U⊗d(g)t=t for all g∈G},

and the Haar average is the matrix P=∫GU⊗d(g) dμ(g)P=\int_GU^{\otimes d}(g)\,d\mu(g)P=∫G​U⊗d(g)dμ(g). Given vectors a1,…,am∈H⊗da_1,\dots,a_m\in H^{\otimes d}a1​,…,am​∈H⊗d, let A=[⟨ei,ax⟩]A=[\langle e_i,a_x\rangle]A=[⟨ei​,ax​⟩] be the Nd×mN^d\times mNd×m matrix whose columns are their coordinates, A∗A^*A∗ its conjugate transpose, A∗A=[⟨ax,ay⟩]A^*A=[\langle a_x,a_y\rangle]A∗A=[⟨ax​,ay​⟩] their Gram matrix, and, when it is invertible, W=(A∗A)−1W=(A^*A)^{-1}W=(A∗A)−1 the Weingarten matrix of a1,…,ama_1,\dots,a_ma1​,…,am​.

Formalization targets

Goal: Theorem 2.1 (p. 4)

If a1,…,ama_1,\dots,a_ma1​,…,am​ is a basis of (H⊗d)G(H^{\otimes d})^G(H⊗d)G, then for all i,j∈Fun(d,N)i,j\in\mathrm{Fun}(d,N)i,j∈Fun(d,N)

Iij=∑x,y=1mAix Wxy Ayj∗.I_{ij}=\sum_{x,y=1}^mA_{ix}\,W_{xy}\,A^*_{yj}.Iij​=x,y=1∑m​Aix​Wxy​Ayj∗​.

The identity is exact and holds for every compact group, every continuous unitary representation, every degree ddd and every choice of basis; nothing is fixed beyond the data.

Milestones (Section 2, pp. 3–4)

  1. Iij=PijI_{ij}=P_{ij}Iij​=Pij​: the Weingarten integrals are the matrix elements of the Haar average.
  2. P∗=PP^*=PP∗=P, P2=PP^2=PP2=P, and im⁡P=(H⊗d)G\operatorname{im}P=(H^{\otimes d})^GimP=(H⊗d)G: PPP is the orthogonal projection onto the invariant tensors (the existing platform theorem that a Haar average of a compact group representation is an idempotent fixing exactly the invariant vectors is included as a supporting reference item).
  3. For linearly independent columns, the selfadjoint idempotent with image span⁡{ax}\operatorname{span}\{a_x\}span{ax​} is A(A∗A)−1A∗A(A^*A)^{-1}A^*A(A∗A)−1A∗.

Significance

Theorem 2.1 reduces a family of analytic problems — Haar integrals of polynomials in matrix entries — to two algebraic ones: find a basis of the invariant tensors of GGG in H⊗dH^{\otimes d}H⊗d (classical invariant theory), and invert its Gram matrix. For U(N)U(N)U(N) the invariants come from Schur–Weyl duality and the Weingarten matrix becomes the Weingarten function on permutations; for O(N)O(N)O(N) and Sp(N)Sp(N)Sp(N) the invariants are indexed by pair partitions; for the symmetric group S(N)S(N)S(N) acting by permutation matrices the invariants are indicators of set partitions. Each of these case-specific formulas, surveyed in Sections 3–5 of the paper, is an instance of Theorem 2.1, so a formal proof of the theorem is the common entry point for formalizing any of them.

The theorem is classical and its proof is elementary once stated; it is not open. No machine-checked proof is known to exist in Mathlib or on this platform: Mathlib has Haar measure, unitary groups and Gram matrices (including the equivalence of linear independence with invertibility of the Gram matrix), but not the averaging projection of a representation in matrix form nor the formula A(A∗A)−1A∗A(A^*A)^{-1}A^*A(A∗A)−1A∗ for orthogonal projections. The mission produces both as reusable lemmas and the Fundamental Theorem itself.

Difficulty

The argument is short on paper; the formal work sits in three places. First, selfadjointness of PPP needs ∫GF(g−1) dμ=∫GF(g) dμ\int_GF(g^{-1})\,d\mu=\int_GF(g)\,d\mu∫G​F(g−1)dμ=∫G​F(g)dμ, i.e. unimodularity of a compact group, which Mathlib does not provide as an instance for non-commutative groups. Second, identifying the image of PPP with the invariant tensors needs both directions: invariant vectors are fixed by PPP, and every vector PtPtPt is invariant, which uses left invariance of μ\muμ inside a matrix-valued Bochner integral. Third, the projection formula needs invertibility of A∗AA^*AA∗A from linear independence and an argument that a selfadjoint idempotent is determined by its image. Commuting the integral with matrix entries and products is routine but must be done with care for the matrix norm chosen.

Formalization scope

  • HHH is CN\mathbb C^NCN with its standard basis (a finite-dimensional Hilbert space with a specified orthonormal basis is the same thing after identification); UUU is a monoid homomorphism G →* Matrix.unitaryGroup (Fin N) ℂ, assumed continuous. Indices are 0-based: [N][N][N] is Fin N, multi-indices are Fin d → Fin N, tensors are (Fin d → Fin N) → ℂ.
  • GGG carries [IsTopologicalGroup G] [CompactSpace G] [T2Space G] [BorelSpace G], and μ\muμ is a Haar measure that is a probability measure. Right invariance is not assumed; it is a consequence that solvers must prove.
  • ddd ranges over all of N\mathbb NN, including d=0d=0d=0, where both sides equal 111; the paper takes ddd positive.
  • The inner product is conjugate-linear in the first slot, so Ayj∗=Ajy‾A^*_{yj}=\overline{A_{jy}}Ayj∗​=Ajy​​ and A∗A^*A∗ is Mathlib's conjugate transpose.
  • PPP is a Bochner integral of matrices, taken with the entrywise sup norm; in finite dimension the integral is independent of the norm.
  • The basis hypothesis is two hypotheses: LinearIndependent ℂ a and span (range a) = invariants U d, with the invariant subspace defined from the representation. Without independence Mathlib's matrix inverse returns 000 on a singular Gram matrix, and the statement would be false; it is therefore not a trivializing gap but a necessary hypothesis.
  • Correction. The paper says "a basis a1,…,ama_1,\dots,a_ma1​,…,am​ of (H)⊗d(H)^{\otimes d}(H)⊗d"; the same paragraph calls the axa_xax​ "the basic invariants", and with a basis of all of H⊗dH^{\otimes d}H⊗d the right-hand side would be the identity. The formal statement uses a basis of (H⊗d)G(H^{\otimes d})^G(H⊗d)G.
  • A trivializing formalization — defining the Weingarten integral as AWA∗AWA^*AWA∗, defining the invariant space as the image of PPP, or assuming that PPP projects onto the span of the axa_xax​ — is excluded: the left side is the Haar integral of the product of matrix elements written out, and the invariant space is defined by U⊗d(g)t=tU^{\otimes d}(g)t=tU⊗d(g)t=t.

Needed infrastructure: matrix-valued Bochner integrals commuting with entries and with matrix multiplication; unimodularity of compact groups; the orthogonal-projection formula. All three are reusable well beyond this mission, and contributions of these general lemmas are welcome.

Selected references

  • B. Collins, S. Matsumoto, J. Novak, The Weingarten Calculus, arXiv preprint, 2021. https://arxiv.org/abs/2109.14890
  • D. Weingarten, Asymptotic behavior of group integrals in the limit of infinite rank, J. Math. Phys. 19 (1978), 999–1001. https://doi.org/10.1063/1.523807
  • B. Collins, Moments and cumulants of polynomial random variables on unitary groups, the Itzykson–Zuber integral, and free probability, Int. Math. Res. Not. 2003, no. 17, 953–982. https://doi.org/10.1155/S107379280320917X
  • B. Collins, P. Śniady, Integration with respect to the Haar measure on unitary, orthogonal and symplectic group, Comm. Math. Phys. 264 (2006), 773–795. https://doi.org/10.1007/s00220-006-1554-3
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Category TheoryDifferential GeometryMathematical Physics·Captain: mikedeng1

Categorified Symplectic Geometry and the Classical String: The Hamiltonian 1-Forms of a 2-Plectic Space Form a Hemistrict Lie 2-AlgebraResearch Paper

Motivation

Classical Hamiltonian mechanics describes an observable by a function on a phase space. Its Poisson bracket gives observables a Lie algebra: the bracket is antisymmetric and satisfies the Jacobi identity. In multisymplectic geometry, a higher-degree differential form replaces the symplectic two-form, and the appropriate observables may themselves be differential forms. This occurs in the geometric description of classical field theories, where quantities associated with spatially extended objects are naturally expressed through forms rather than functions. Baez, Hoffnung and Rogers use this setting to examine the classical bosonic string in their paper, §§1 and 5.

The same paper shows why the usual Lie algebra of observables is too narrow for a 2-plectic space. One bracket of Hamiltonian one-forms satisfies the Jacobi identity but is antisymmetric only up to an exact form; another is antisymmetric but has a Jacobi identity only up to an exact form. Theorem 4.3 packages the first bracket and its exact correction into a hemistrict Lie 2-algebra. The result is known mathematically; the mission is to give its construction a machine-checked statement and proof in Lean for a finite-dimensional vector-space model Baez–Hoffnung–Rogers, §§3–4.

Setting

Let EEE be a finite-dimensional real normed vector space. A 2-plectic form ω\omegaω on EEE is a smooth three-form with dω=0d\omega=0dω=0 such that ιvω=0\iota_v\omega=0ιv​ω=0 implies v=0v=0v=0 at each point. The interior product ιv\iota_vιv​ places vvv in the first argument of the form. This is the n=2n=2n=2 case of Definition 2.1 in the preprint, p. 6.

A smooth one-form FFF is Hamiltonian when a smooth vector field vFv_FvF​ satisfies

dF=−ιvFω.dF=-\iota_{v_F}\omega.dF=−ιvF​​ω.

Nondegeneracy makes vFv_FvF​ unique. Write Ham⁡(E,ω)\operatorname{Ham}(E,\omega)Ham(E,ω) for the vector space of these forms. The hemi-bracket is {F,G}h=LvFG\{F,G\}_h=\mathcal L_{v_F}G{F,G}h​=LvF​​G, where Lv\mathcal L_vLv​ is the Lie derivative. On functions its left and right actions are {F,f}h=LvFf\{F,f\}_h=\mathcal L_{v_F}f{F,f}h​=LvF​​f and {f,F}h=0\{f,F\}_h=0{f,F}h​=0. The alternator is the function SF,G=−(ιvFG+ιvGF)S_{F,G}=-(\iota_{v_F}G+\iota_{v_G}F)SF,G​=−(ιvF​​G+ιvG​​F). These formulas are Definitions 3.1 and 3.3 and the data preceding Theorem 4.3 on pp. 11 and 16–17.

A Lie 2-algebra here has a two-term chain complex L0←dL1L_0\xleftarrow{d}L_1L0​d​L1​, a bracket on its available degrees, an alternator witnessing antisymmetry up to chain homotopy, and a Jacobiator witnessing the Jacobi relation up to chain homotopy. Four further coherence diagrams constrain these homotopies. “Hemistrict” means that the Jacobiator is the identity homotopy, represented by the zero degree-one map Definitions 4.1–4.2, pp. 15–16.

Formalization targets

Hemistrict structure on Hamiltonian one-forms

For every 2-plectic (E,ω)(E,\omega)(E,ω), Theorem 4.3 constructs a hemistrict Lie 2-algebra with all components fixed:

L0=Ham⁡(E,ω),L1=C∞(E),dLf=df,[F,G]={F,G}h,L_0=\operatorname{Ham}(E,\omega),\qquad L_1=C^\infty(E),\qquad d_L f=df,\qquad [F,G]=\{F,G\}_h,L0​=Ham(E,ω),L1​=C∞(E),dL​f=df,[F,G]={F,G}h​, [F,f]=LvFf,[f,F]=0,SF,G=−(ιvFG+ιvGF),JF,G,H=0.[F,f]=\mathcal L_{v_F}f,\qquad [f,F]=0,\qquad S_{F,G}=-(\iota_{v_F}G+\iota_{v_G}F),\qquad J_{F,G,H}=0.[F,f]=LvF​​f,[f,F]=0,SF,G​=−(ιvF​​G+ιvG​​F),JF,G,H​=0.

The existential goal fixes the carriers and every operation; the Lie 2-algebra structure then requires the chain-map identities, the homotopy identities, and all four coherence equations. The milestones isolate the source statements that supply the main geometric identities: uniqueness of vFv_FvF​, Hamiltonian exact forms, Proposition 3.6, the bracket’s chain-map behavior, a mixed Jacobi identity, and the third coherence diagram Theorem 4.3 and its proof, pp. 17–18.

Significance

The theorem identifies the algebraic structure carried by 2-plectic observables. In particular, it retains the exact-form discrepancy in antisymmetry as an explicit operation instead of losing it by quotienting forms. The paper compares this hemistrict construction with a semistrict construction and then studies their relation; the present result is the first of those two constructions Theorems 4.3–4.6, pp. 17–19.

A formal proof would connect Mathlib’s exterior derivative on alternating forms to a reusable calculus of interior products, Lie derivatives, Hamiltonian vector fields, and higher algebraic coherence. The preprint already proves the theorem. The local proposal supplies statements for its finite-dimensional normed-space case; a proof in Lean and an extension from vector spaces to general smooth manifolds are further work. The current mission does not assert the separate semistrict theorem or the string application.

Difficulty

The obvious attempt to make Hamiltonian one-forms into a conventional Lie algebra fails: for the hemi-bracket, {F,G}h+{G,F}h\{F,G\}_h+\{G,F\}_h{F,G}h​+{G,F}h​ can be a nonzero exact form. The correction must also agree with the bracket on functions and with the Jacobi relation across degrees. A degree-zero calculation alone therefore does not establish a Lie 2-algebra. Its chain-map conditions and four coherence diagrams impose further identities involving the exterior derivative, Lie derivatives and the Hamiltonian vector fields Proposition 3.6 and Definition 4.1, pp. 12, 15–16.

Formalization scope

Lean uses EEE with NormedAddCommGroup, NormedSpace ℝ E and FiniteDimensional ℝ E, in place of the paper’s arbitrary smooth manifold XXX. Forms, functions and vector fields are defined on all of EEE, and smoothness means ContDiff ℝ smooth with smooth the C∞C^\inftyC∞ degree. This is the disclosed scope cut. The Lean name for the paper’s ω\omegaω is Ω; this avoids a notation conflict from the imported definition module.

Mathlib represents a kkk-form as a function into a continuous alternating kkk-linear map. Its extDeriv supplies the exterior derivative, while the published ContWeatherall.grad supplies dfdfdf for functions. Interior product inserts in the first slot. The Lie derivative of a one-form uses its derivative and the derivative of the vector field, so Cartan’s identity is a mathematical assertion rather than the definition of the Lie derivative. The bracket of vector fields uses [V,W]=DW⋅V−DV⋅W[V,W]=DW\cdot V-DV\cdot W[V,W]=DW⋅V−DV⋅W. The two-term chain complex uses the paper’s ordinary graded tensor-product differential and signed switch. Every formal statement about hamVF restricts its form to Hamiltonian inputs, where nondegeneracy gives a unique vector field; the default value on other inputs has no role in the theorem.

The goal requires all operations to have the paper’s values and requires every Lie 2-algebra axiom. An arbitrary pre-existing Lie 2-algebra or a structure with unconstrained operations would not establish this result. Reusable contributions include differential-form identities and the algebraic two-term interface. The separate semi-bracket construction and general-manifold extension remain outside this proposal.

Selected references

  • J. C. Baez, A. E. Hoffnung and C. L. Rogers, Categorified Symplectic Geometry and the Classical String, preprint arXiv:0808.0246v1, 2008; published in Communications in Mathematical Physics 293 (2010), 701–725. Preprint.
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Mathematical PhysicsQuantum InformationRepresentation Theory·Captain: mikedeng1

SU(2)-Irreducibly Covariant and EPOSIC Channels: EPOSIC Channels Are Exactly the Extreme Points of the Convex Set of SU(2)-Irreducibly Covariant ChannelsResearch Paper

Motivation

A quantum channel is a completely positive, trace-preserving linear map between matrix algebras; it models the most general physical transformation of a quantum system. When a group GGG acts on the input and output systems, a channel is GGG-covariant if it commutes with the group action. Covariant channels appear whenever a physical process respects a symmetry: rotation-invariant noise on spin systems, optimal cloning and estimation of spin states, and quantum reference frames for orientation. Because the covariant channels between two fixed representations form a convex set, the first structural question is what its extreme points are; they are the channels from which every covariant channel is obtained by mixing.

For G=SU(2)G = SU(2)G=SU(2) acting irreducibly on input and output, the tensor product of the output representation with the dual of the input one is multiplicity free, so the commutant that controls covariant maps is abelian (Vollbrecht and Werner, 2001, arXiv:quant-ph/0010095). M. Al Nuwairan (arXiv:1306.5321, 2013) makes this concrete. She constructs explicit channels Φm,n,h\Phi_{m,n,h}Φm,n,h​, called EPOSIC channels, from Clebsch–Gordan isometries realized on polynomials. She computes their Choi matrices and shows that they are exactly the extreme points of the convex set of SU(2)-irreducibly covariant channels.

Setting

For k∈Nk \in \mathbb Nk∈N, let PkP_kPk​ be the space of homogeneous polynomials of degree kkk in two variables x1,x2x_1, x_2x1​,x2​. The group

SU(2)={(ab−bˉaˉ):a,b∈C, ∣a∣2+∣b∣2=1}SU(2)=\left\{\begin{pmatrix}a&b\\-\bar b&\bar a\end{pmatrix}: a,b\in\mathbb C,\ |a|^2+|b|^2=1\right\}SU(2)={(a−bˉ​baˉ​):a,b∈C, ∣a∣2+∣b∣2=1}

acts on PkP_kPk​ by the representation ρk\rho_kρk​,

(ρk(g)f)(x1,x2)=f((x1,x2)g)=f(ax1−bˉx2, bx1+aˉx2).(\rho_k(g)f)(x_1,x_2)=f\big((x_1,x_2)g\big)=f(ax_1-\bar bx_2,\ bx_1+\bar ax_2).(ρk​(g)f)(x1​,x2​)=f((x1​,x2​)g)=f(ax1​−bˉx2​, bx1​+aˉx2​).

With the inner product ⟨x1lx2k−l,x1jx2k−j⟩=l!(k−l)! δlj\langle x_1^lx_2^{k-l},x_1^jx_2^{k-j}\rangle=l!(k-l)!\,\delta_{lj}⟨x1l​x2k−l​,x1j​x2k−j​⟩=l!(k−l)!δlj​, the canonical basis flk=x1lx2k−l/l!(k−l)!f^k_l=x_1^lx_2^{k-l}/\sqrt{l!(k-l)!}flk​=x1l​x2k−l​/l!(k−l)!​, 0≤l≤k0\le l\le k0≤l≤k, is orthonormal and ρk\rho_kρk​ is unitary. The ρk\rho_kρk​ are the irreducible representations of SU(2)SU(2)SU(2).

The tensor product Pm⊗PnP_m\otimes P_nPm​⊗Pn​ is realized as the polynomials in x=(x1,x2)x=(x_1,x_2)x=(x1​,x2​) and y=(y1,y2)y=(y_1,y_2)y=(y1​,y2​) of bi-degree (m,n)(m,n)(m,n). On these polynomials act the operators

Δxy=x1∂y1+x2∂y2,Δyx=y1∂x1+y2∂x2,Γxy=(x1y2−y1x2)⋅,Ωxy=∂x1∂y2−∂x2∂y1.\Delta_{xy}=x_1\partial_{y_1}+x_2\partial_{y_2},\quad \Delta_{yx}=y_1\partial_{x_1}+y_2\partial_{x_2},\quad \Gamma_{xy}=(x_1y_2-y_1x_2)\cdot,\quad \Omega_{xy}=\partial_{x_1}\partial_{y_2}-\partial_{x_2}\partial_{y_1}.Δxy​=x1​∂y1​​+x2​∂y2​​,Δyx​=y1​∂x1​​+y2​∂x2​​,Γxy​=(x1​y2​−y1​x2​)⋅,Ωxy​=∂x1​​∂y2​​−∂x2​​∂y1​​.

Real coefficients cm,n,hc_{m,n,h}cm,n,h​ are defined by the recursion cm,0,0=1c_{m,0,0}=1cm,0,0​=1 and, for n≥1n\ge 1n≥1, cm,n,h=1(m+1)n cm+1,n−1,hc_{m,n,h}=\frac{1}{(m+1)n}\,c_{m+1,n-1,h}cm,n,h​=(m+1)n1​cm+1,n−1,h​ if h=0h=0h=0, 1(m+1)n (cm−1,n−1,h−1+cm+1,n−1,h)\frac{1}{(m+1)n}\,(c_{m-1,n-1,h-1}+c_{m+1,n-1,h})(m+1)n1​(cm−1,n−1,h−1​+cm+1,n−1,h​) if 0<h<n0<h<n0<h<n, and 1(m+1)n cm−1,n−1,h−1\frac{1}{(m+1)n}\,c_{m-1,n-1,h-1}(m+1)n1​cm−1,n−1,h−1​ if h=nh=nh=n. For 0≤h≤min⁡{m,n}0\le h\le\min\{m,n\}0≤h≤min{m,n} and r=m+n−2hr=m+n-2hr=m+n−2h, the map

αm,n,h:Pr→Pm⊗Pn,αm,n,h(f)=cm,n,h  ΓxyhΔyxn−hf\alpha_{m,n,h}:P_r\to P_m\otimes P_n,\qquad \alpha_{m,n,h}(f)=\sqrt{c_{m,n,h}}\;\Gamma_{xy}^h\Delta_{yx}^{n-h}fαm,n,h​:Pr​→Pm​⊗Pn​,αm,n,h​(f)=cm,n,h​​Γxyh​Δyxn−h​f

is an explicit Clebsch–Gordan isometry. The EPOSIC channel is

Φm,n,h(A)=TrPn(αm,n,hAαm,n,h∗),A∈End(Pr).\Phi_{m,n,h}(A)=\mathrm{Tr}_{P_n}\big(\alpha_{m,n,h}A\alpha_{m,n,h}^*\big),\qquad A\in\mathrm{End}(P_r).Φm,n,h​(A)=TrPn​​(αm,n,h​Aαm,n,h∗​),A∈End(Pr​).

For r,m∈Nr,m\in\mathbb Nr,m∈N, EC(r,m)EC(r,m)EC(r,m) is the set of the channels Φm,n,h\Phi_{m,n,h}Φm,n,h​ with r=m+n−2hr=m+n-2hr=m+n−2h and 0≤h≤min⁡{m,n}0\le h\le\min\{m,n\}0≤h≤min{m,n}. The set QCSU(2)(ρr,ρm)QC_{SU(2)}(\rho_r,\rho_m)QCSU(2)​(ρr​,ρm​) consists of the quantum channels Φ:End(Pr)→End(Pm)\Phi:\mathrm{End}(P_r)\to\mathrm{End}(P_m)Φ:End(Pr​)→End(Pm​) with Φ(ρr(g)Aρr(g)∗)=ρm(g)Φ(A)ρm(g)∗\Phi(\rho_r(g)A\rho_r(g)^*)=\rho_m(g)\Phi(A)\rho_m(g)^*Φ(ρr​(g)Aρr​(g)∗)=ρm​(g)Φ(A)ρm​(g)∗ for all AAA and all g∈SU(2)g\in SU(2)g∈SU(2).

Formalization targets

Goal: Proposition 4.1

ext⁡QCSU(2)(ρr,ρm)=EC(r,m)for all r,m∈N.\operatorname{ext} QC_{SU(2)}(\rho_r,\rho_m)=EC(r,m)\qquad\text{for all } r,m\in\mathbb N.extQCSU(2)​(ρr​,ρm​)=EC(r,m)for all r,m∈N.

The statement has no hypotheses and covers the degenerate cases: for min⁡{r,m}=0\min\{r,m\}=0min{r,m}=0 the set EC(r,m)EC(r,m)EC(r,m) is a single channel.

Milestones

In the order of the paper's argument:

  1. the polynomial decomposition identity behind cm,n,hc_{m,n,h}cm,n,h​ (Theorem 2.7), the adjoint of αm,n,h\alpha_{m,n,h}αm,n,h​ and the resolution of the identity ∑hαm,n,hαm,n,h∗=I\sum_h\alpha_{m,n,h}\alpha_{m,n,h}^*=I∑h​αm,n,h​αm,n,h∗​=I (Lemma 2.9);
  2. αm,n,h\alpha_{m,n,h}αm,n,h​ is an SU(2)-equivariant isometry (Proposition 2.10), so Φm,n,h∈QCSU(2)(ρr,ρm)\Phi_{m,n,h}\in QC_{SU(2)}(\rho_r,\rho_m)Φm,n,h​∈QCSU(2)​(ρr​,ρm​) (Proposition 2.18), and EC(r,m)={Φm,r+m−2l,m−l:0≤l≤min⁡{r,m}}EC(r,m)=\{\Phi_{m,r+m-2l,m-l}:0\le l\le\min\{r,m\}\}EC(r,m)={Φm,r+m−2l,m−l​:0≤l≤min{r,m}} (Proposition 2.21);
  3. the orthogonal decomposition of Pm⊗PˉnP_m\otimes\bar P_nPm​⊗Pˉn​ into irreducible subspaces with projections qqq (Corollary 2.13), covariance in terms of the Choi matrix (Proposition 1.15(1)), and the commutant of ρm⊗ρˇr\rho_m\otimes\check\rho_rρm​⊗ρˇ​r​ (Proposition 3.9);
  4. the Choi matrix C(Φm,n,h)=r+1n+1qm,r,m−hC(\Phi_{m,n,h})=\frac{r+1}{n+1}q_{m,r,m-h}C(Φm,n,h​)=n+1r+1​qm,r,m−h​ (Lemma 3.12, Proposition 3.13);
  5. EC(r,m)EC(r,m)EC(r,m) spans the SU(2)-equivariant linear maps (Corollary 3.16), and QCSU(2)(ρr,ρm)=conv⁡EC(r,m)QC_{SU(2)}(\rho_r,\rho_m)=\operatorname{conv}EC(r,m)QCSU(2)​(ρr​,ρm​)=convEC(r,m) (Corollary 3.17).

Significance

The result shows that the covariant channels between two irreducible SU(2) representations form a simplex. Its min⁡{r,m}+1\min\{r,m\}+1min{r,m}+1 vertices are given by closed formulas, so optimization problems over SU(2)-covariant channels reduce to finite-dimensional linear programs over the vertex weights: fidelities, output entropies, capacities restricted to covariant encodings. The explicit Choi matrices make the vertices computable in any basis. The same channels are used later in the paper to compute complementary channels and to exhibit a positive map that is not completely positive.

On the formal side, the mission produces the irreducible representations of SU(2)SU(2)SU(2) on homogeneous polynomials, the polarization operators, and explicit Clebsch–Gordan isometries with their equivariance and isometry proofs. It also produces a Choi-matrix criterion for covariance and the extreme-point characterization. Theorem 2.7, Proposition 1.15 and Proposition 3.9 are cited in the paper from Procesi, from [4] and from Vollbrecht–Werner, and are proved neither there nor, as far as is known, in any proof assistant. Nothing in this mission is machine-checked yet.

Difficulty

That Φm,n,h\Phi_{m,n,h}Φm,n,h​ is a covariant channel follows from equivariance of αm,n,h\alpha_{m,n,h}αm,n,h​. The real content lies elsewhere.

First, αm,n,h\alpha_{m,n,h}αm,n,h​ is an isometry only because of the specific constants cm,n,hc_{m,n,h}cm,n,h​. The paper derives this from the polynomial identity of Theorem 2.7 and Schur's lemma, and neither is available off the shelf for these polynomial models.

Second, the extreme-point statement needs the full commutant of ρm⊗ρˉr\rho_m\otimes\bar\rho_rρm​⊗ρˉ​r​: the projections onto the irreducible summands must span it. Exhibiting the projections is not enough; one must show there is nothing else, which is multiplicity-freeness of the tensor product. This requires the irreducibility of each ρk\rho_kρk​ and the Clebsch–Gordan dimension count.

Third, matching each EPOSIC channel with a single projection (Proposition 3.13) requires a rank argument. The obvious approach, expanding the Choi matrix in coordinates, gives sums of products of signed binomial coefficients whose non-vanishing is the content of Lemma 3.12.

Formalization scope

Every space PkP_kPk​ is represented by coordinates in its canonical orthonormal basis, indexed by Fin (k+1) with 0-based indices. So End(Pk)\mathrm{End}(P_k)End(Pk​) is the algebra of (k+1)×(k+1)(k+1)\times(k+1)(k+1)×(k+1) complex matrices, and adjoints for the inner product above are conjugate transposes. The representation ρk\rho_kρk​ and the maps αm,n,h\alpha_{m,n,h}αm,n,h​ are computed from the polynomials themselves in MvPolynomial (Fin 4) ℂ (variables x1,x2,y1,y2x_1,x_2,y_1,y_2x1​,x2​,y1​,y2​, derivatives pderiv), then read off in the canonical bases. The action is the row-vector action f↦f((x1,x2)g)f\mapsto f((x_1,x_2)g)f↦f((x1​,x2​)g) of the paper. Pm⊗PnP_m\otimes P_nPm​⊗Pn​ is indexed with the PmP_mPm​ factor first. The conjugate space Pˉk\bar P_kPˉk​ has the same coordinates and carries the entrywise conjugate matrices. The coefficients cm,n,hc_{m,n,h}cm,n,h​ are defined by the recursion, not by any closed formula. The input degree rrr of α\alphaα is an explicit argument, constrained by r+2h=m+nr+2h=m+nr+2h=m+n in every statement.

Channels are maps on matrices given by a finite normalized Kraus family (the published QAlgorithms.IsChannel). The partial trace is the published WildeQIT.partialTraceRight, over the PnP_nPn​ factor. The Choi matrix is ∑ijΦ(Eij)⊗Eij\sum_{ij}\Phi(E_{ij})\otimes E_{ij}∑ij​Φ(Eij​)⊗Eij​ with the output factor first. Convex hulls and extreme points are taken over R\mathbb RR in the space of maps.

No statement replaces ρk\rho_kρk​ by an abstract irreducible representation. None defines EC(r,m)EC(r,m)EC(r,m) through extreme points, Choi matrices or projections, defines αm,n,h\alpha_{m,n,h}αm,n,h​ by its coefficient expansion, or assumes Schur's lemma, the commutant description or Theorem 2.7 as a hypothesis. Each of these would make the goal or a milestone true by definition.

A complete development needs:

  • the irreducibility and unitarity of ρk\rho_kρk​;
  • Schur's lemma for these matrix representations;
  • the Clebsch–Gordan decomposition and the polynomial identities of Theorem 2.7;
  • Kraus/Stinespring/Choi correspondences for finite-dimensional channels.

The SU(2) representation layer and the Choi-matrix covariance criterion are reusable beyond this mission. Proofs of the cited results (Theorem 2.7, Propositions 1.15 and 3.9) are welcome as independent contributions.

Selected references

  • M. Al Nuwairan, SU(2)-irreducibly covariant and EPOSIC channels, arXiv preprint, 2013. https://arxiv.org/abs/1306.5321
  • K. G. H. Vollbrecht and R. F. Werner, Entanglement measures under symmetry, Phys. Rev. A 64, 062307, 2001. https://arxiv.org/abs/quant-ph/0010095
  • C. Procesi, Lie Groups: An Approach through Invariants and Representations, Springer Universitext, 2007. https://doi.org/10.1007/978-0-387-28929-8
  • J. Watrous, The Theory of Quantum Information, Cambridge University Press, 2018. https://doi.org/10.1017/9781316848142
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Linear algebraQuantum InformationRepresentation Theory·Captain: mikedeng1

Commutant Structure of U⊗(n−1) ⊗ U* Transformations: The Operator Basis v^{ab}_{ij}(α) Is a Linear Combination of Partially Transposed Permutation Operators, and ConverselyResearch Paper

Motivation

Operators on nnn copies of Cd\mathbb C^dCd that commute with every U⊗nU^{\otimes n}U⊗n, UUU unitary, are exactly the linear combinations of permutation operators V(σ)V(\sigma)V(σ), σ∈S(n)\sigma\in S(n)σ∈S(n) (Schur–Weyl duality). Many protocols in quantum information carry a different symmetry: the first n−1n-1n−1 systems transform by UUU and the last by the complex conjugate Uˉ\bar UUˉ. It is the symmetry of port-based teleportation (Ishizaka–Hiroshima 2008), whose optimal performance is computed from the representation theory of exactly this commutant (Studziński et al. 2017). The commutant of U⊗(n−1)⊗UˉU^{\otimes(n-1)}\otimes\bar UU⊗(n−1)⊗Uˉ is the algebra A\mathcal AA spanned by the partially transposed permutation operators V′(σ)V'(\sigma)V′(σ): the permutation operators with the transpose taken on the last system.

Studziński, Horodecki and Mozrzymas give an explicit operator basis of an ideal of A\mathcal AA adapted to the irreducible representations of S(n−2)S(n-2)S(n−2). Their main structural result, Theorem 9, states that this basis and the partially transposed permutation operators are linear combinations of each other, with coefficients that are irrep matrix elements.

Setting

Let H=Cd\mathcal H=\mathbb C^dH=Cd and n≥2n\ge2n≥2. For σ∈S(n)\sigma\in S(n)σ∈S(n), the permutation operator V(σ)V(\sigma)V(σ) on H⊗n\mathcal H^{\otimes n}H⊗n is V(σ)∣i1…in⟩=∣iσ−1(1)…iσ−1(n)⟩V(\sigma)|i_1\dots i_n\rangle=|i_{\sigma^{-1}(1)}\dots i_{\sigma^{-1}(n)}\rangleV(σ)∣i1​…in​⟩=∣iσ−1(1)​…iσ−1(n)​⟩. The partial transposition X↦X′X\mapsto X'X↦X′ transposes the last system: (∣x1…xn⟩⟨y1…yn∣)′=∣x1…xn−1yn⟩⟨y1…yn−1xn∣(|x_1\dots x_n\rangle\langle y_1\dots y_n|)'=|x_1\dots x_{n-1}y_n\rangle\langle y_1\dots y_{n-1}x_n|(∣x1​…xn​⟩⟨y1​…yn​∣)′=∣x1​…xn−1​yn​⟩⟨y1​…yn−1​xn​∣. The algebra A\mathcal AA is the span of all V′(τ)V'(\tau)V′(τ), τ∈S(n)\tau\in S(n)τ∈S(n). S(k)S(k)S(k) is regarded inside S(n)S(n)S(n) as the permutations fixing the systems after kkk.

Fix π1,…,πn−1∈S(n−1)\pi_1,\dots,\pi_{n-1}\in S(n-1)π1​,…,πn−1​∈S(n−1) with πk(n−1)=k\pi_k(n-1)=kπk​(n−1)=k, and write Πk=V(πk)\Pi_k=V(\pi_k)Πk​=V(πk​). For labels a,b∈{1,…,n−1}a,b\in\{1,\dots,n-1\}a,b∈{1,…,n−1}, the maps fab:S(n−2)→S(n)f_{ab}:S(n-2)\to S(n)fab​:S(n−2)→S(n) are

fab(σ)=πb∘σ∘(n n−1)∘πa−1.f_{ab}(\sigma)=\pi_b\circ\sigma\circ(n\ n-1)\circ\pi_a^{-1}.fab​(σ)=πb​∘σ∘(n n−1)∘πa−1​.

The standing data are a family of real orthogonal, irreducible, pairwise inequivalent matrix representations φα\varphi^\alphaφα of S(n−2)S(n-2)S(n−2), of dimensions dαd_\alphadα​, and a Schur basis: an orthonormal basis ∣ϕi(α,r)⟩|\phi_i(\alpha,r)\rangle∣ϕi​(α,r)⟩ of H⊗(n−2)\mathcal H^{\otimes(n-2)}H⊗(n−2), where rrr ranges over the multiplicity of α\alphaα, such that V(σ)∣ϕj(α,r)⟩=∑iφijα(σ)∣ϕi(α,r)⟩V(\sigma)|\phi_j(\alpha,r)\rangle=\sum_i\varphi^\alpha_{ij}(\sigma)|\phi_i(\alpha,r)\rangleV(σ)∣ϕj​(α,r)⟩=∑i​φijα​(σ)∣ϕi​(α,r)⟩. With ∣Φ+⟩=1d∑l∣ll⟩|\Phi^+\rangle=\frac1{\sqrt d}\sum_l|ll\rangle∣Φ+⟩=d​1​∑l​∣ll⟩ on the last two systems, the paper defines

∣ψik(α,r)⟩=d Πk∣ϕi(α,r)⟩∣Φ+⟩,vijab(α)=∑r∣ψia(α,r)⟩⟨ψjb(α,r)∣,|\psi^k_i(\alpha,r)\rangle=\sqrt d\,\Pi_k|\phi_i(\alpha,r)\rangle|\Phi^+\rangle,\qquad v^{ab}_{ij}(\alpha)=\sum_r|\psi^a_i(\alpha,r)\rangle\langle\psi^b_j(\alpha,r)|,∣ψik​(α,r)⟩=d​Πk​∣ϕi​(α,r)⟩∣Φ+⟩,vijab​(α)=r∑​∣ψia​(α,r)⟩⟨ψjb​(α,r)∣,

and Eijα=∑r∣ϕi(α,r)⟩⟨ϕj(α,r)∣E^\alpha_{ij}=\sum_r|\phi_i(\alpha,r)\rangle\langle\phi_j(\alpha,r)|Eijα​=∑r​∣ϕi​(α,r)⟩⟨ϕj​(α,r)∣. In Lean these are IrrepFamily, SchurBasis, psi, vOp, EOp, with ptLast for ′'′, fPerm for fabf_{ab}fab​ and algA for A\mathcal AA.

Formalization targets

Goal: Theorem 9

For all a,ba,ba,b, α\alphaα, i,ji,ji,j:

vijab(α)=dα(n−2)!∑σ∈S(n−2)φijα(σ) V′(fba(σ))∈A,v^{ab}_{ij}(\alpha)=\frac{d_\alpha}{(n-2)!}\sum_{\sigma\in S(n-2)}\varphi^\alpha_{ij}(\sigma)\,V'\big(f_{ba}(\sigma)\big)\in\mathcal A,vijab​(α)=(n−2)!dα​​σ∈S(n−2)∑​φijα​(σ)V′(fba​(σ))∈A,

and for every σ∈S(n−2)\sigma\in S(n-2)σ∈S(n−2), with σab=fab(σ)\sigma_{ab}=f_{ab}(\sigma)σab​=fab​(σ),

V′(σab)=⨁α∑i,jφijα(fab−1(σab)) vijba(α).V'(\sigma_{ab})=\bigoplus_\alpha\sum_{i,j}\varphi^\alpha_{ij}\big(f_{ab}^{-1}(\sigma_{ab})\big)\,v^{ba}_{ij}(\alpha).V′(σab​)=α⨁​i,j∑​φijα​(fab−1​(σab​))vijba​(α).

The goal is the whole theorem, both directions and the membership in A\mathcal AA.

Milestones

  1. Permutations of S(n−1)S(n-1)S(n−1) are invariant under ′'′ and pass through it: V(ρ)V′(τ)V(ρ′)=V′(ρτρ′)V(\rho)V'(\tau)V(\rho')=V'(\rho\tau\rho')V(ρ)V′(τ)V(ρ′)=V′(ρτρ′) (proof of Lemma 26).
  2. Lemma 26: V′(σab)=ΠbV(fab−1(σab))V′(n n−1)Πa−1V'(\sigma_{ab})=\Pi_bV(f^{-1}_{ab}(\sigma_{ab}))V'(n\ n-1)\Pi_a^{-1}V′(σab​)=Πb​V(fab−1​(σab​))V′(n n−1)Πa−1​ with fab−1(σab)∈S(n−2)f^{-1}_{ab}(\sigma_{ab})\in S(n-2)fab−1​(σab​)∈S(n−2).
  3. Eq. (9): the Sab=fab(S(n−2))S_{ab}=f_{ab}(S(n-2))Sab​=fab​(S(n−2)) and S(n−1)S(n-1)S(n−1) partition S(n)S(n)S(n), and each fabf_{ab}fab​ is injective.
  4. V′(n−1,n)=1⊗∑l,l′∣ll⟩⟨l′l′∣V'(n-1,n)=\mathbb 1\otimes\sum_{l,l'}|ll\rangle\langle l'l'|V′(n−1,n)=1⊗∑l,l′​∣ll⟩⟨l′l′∣.
  5. Eq. (21): Eijα∈C[S(n−2)]E^\alpha_{ij}\in\mathbb C[S(n-2)]Eijα​∈C[S(n−2)] maps to vijab(α)=Πa(Eijα⊗1)V′(n n−1)Πb−1∈Mv^{ab}_{ij}(\alpha)=\Pi_a(E^\alpha_{ij}\otimes\mathbb 1)V'(n\ n-1)\Pi_b^{-1}\in\mathcal Mvijab​(α)=Πa​(Eijα​⊗1)V′(n n−1)Πb−1​∈M.
  6. Eq. (139): the orthogonality relation 1∣G∣∑gφijα(g)φkpβ(g−1)=1dαδαβδipδjk\frac1{|G|}\sum_g\varphi^\alpha_{ij}(g)\varphi^\beta_{kp}(g^{-1})=\frac1{d_\alpha}\delta^{\alpha\beta}\delta_{ip}\delta_{jk}∣G∣1​∑g​φijα​(g)φkpβ​(g−1)=dα​1​δαβδip​δjk​ for G=S(n−2)G=S(n-2)G=S(n−2).
  7. Eq. (86): Eijα=dα(n−2)!∑σφijα(σ)V(σ)E^\alpha_{ij}=\frac{d_\alpha}{(n-2)!}\sum_\sigma\varphi^\alpha_{ij}(\sigma)V(\sigma)Eijα​=(n−2)!dα​​∑σ​φijα​(σ)V(σ).
  8. Eq. (86): V(σ)=⨁α∑ijφijα(σ)EijαV(\sigma)=\bigoplus_\alpha\sum_{ij}\varphi^\alpha_{ij}(\sigma)E^\alpha_{ij}V(σ)=⨁α​∑ij​φijα​(σ)Eijα​.

Significance

Theorem 9 shows that the vijab(α)v^{ab}_{ij}(\alpha)vijab​(α) span the same space as the operators V′(σ)V'(\sigma)V′(σ) with σ(n)≠n\sigma(n)\neq nσ(n)=n. That space is the ideal of A\mathcal AA on which the partial transposition acts non-trivially. Because the vijab(α)v^{ab}_{ij}(\alpha)vijab​(α) multiply like block matrices (Lemma 8 of the paper), the theorem converts questions about A\mathcal AA into finite matrix computations indexed by irreps of S(n−2)S(n-2)S(n−2). The later constructions of the paper rest on this: the Gram matrices Q(α)Q(\alpha)Q(α), the biorthogonal basis and the explicit irreps of A\mathcal AA. The same formalism underlies the eigenvalue analysis of port-based teleportation (Mozrzymas–Studziński–Horodecki 2018).

The result has a published proof but no machine-checked one. This mission adds that check, together with a reusable Lean vocabulary: the partial transpose on one site of (Cd)⊗n(\mathbb C^d)^{\otimes n}(Cd)⊗n, the embeddings S(k)↪S(n)S(k)\hookrightarrow S(n)S(k)↪S(n), and Schur-basis data for permutation operators. It also pins down the conventions that the printed text leaves inconsistent (see Formalization scope).

Difficulty

The algebraic manipulations are short on paper; the work lies in the bookkeeping they hide. Lemma 26 requires checking that πb−1∘σab∘πa∘(n n−1)\pi_b^{-1}\circ\sigma_{ab}\circ\pi_a\circ(n\ n-1)πb−1​∘σab​∘πa​∘(n n−1) fixes both of the last two systems, which uses πk(n−1)=k\pi_k(n-1)=kπk​(n−1)=k in an essential way. The partial transposition is not multiplicative, so every step that moves an operator through ′'′ needs the specific invariance of milestone 1. Eq. (86) is the identification of the Schur-basis matrix units with group-algebra elements; it needs completeness of the basis together with Schur's orthogonality relations for real irreps, and it is false without irreducibility and inequivalence. Mathlib has Schur's lemma but no coordinate form of the orthogonality relations for matrix representations, and nothing on partial transposes of tensor-power permutation operators.

Formalization scope

  • Indexing. The paper's nnn is m+2m+2m+2. S(n−2)S(n-2)S(n−2), S(n−1)S(n-1)S(n−1), S(n)S(n)S(n) are Equiv.Perm (Fin m), Equiv.Perm (Fin (m+1)), Equiv.Perm (Fin (m+2)). Systems are 0-based, so systems n−1,nn-1,nn−1,n are indices m,m+1m,m+1m,m+1, and labels a,ba,ba,b are Fin (m+1). Permutation products compose right to left.
  • Operators are complex matrices indexed by {1..d}k\{1..d\}^k{1..d}k. V(σ)V(\sigma)V(σ) is the published QAlgorithms.Schur.tensorPermutation. ′'′ is on the last system only. groupAlg is the represented C[S(n−2)]\mathbb C[S(n-2)]C[S(n−2)]; algM spans the V′(τ)V'(\tau)V′(τ) with τ(n)≠n\tau(n)\ne nτ(n)=n.
  • Standing data. IrrepFamily m contains real orthogonal matrix representations of S(m)S(m)S(m) of positive dimension, irreducible over C\mathbb CC and pairwise inequivalent over C\mathbb CC (p. 7 and Notation 32 of the paper). SchurBasis F d is an orthonormal, spanning family of vectors on which V(σ)V(\sigma)V(σ) acts by the φα\varphi^\alphaφα. Multiplicities may be zero. The family is not required to list every irrep of S(m)S(m)S(m). Every πk\pi_kπk​ satisfies πk(n−1)=k\pi_k(n-1)=kπk​(n−1)=k (Definition 24).
  • Constants are literal: dα/(n−2)!d_\alpha/(n-2)!dα​/(n−2)!, the factor d\sqrt dd​ of ψ\psiψ and the 1/d1/\sqrt d1/d​ of ∣Φ+⟩|\Phi^+\rangle∣Φ+⟩. ⨁α\bigoplus_\alpha⨁α​ is a sum over all labels.
  • Ruled out. vijab(α)v^{ab}_{ij}(\alpha)vijab​(α) is defined from the vectors ψ\psiψ (Definition 5) and EijαE^\alpha_{ij}Eijα​ from the Schur basis (footnote 4). Neither is defined as a group sum or through Fabt\mathcal F^t_{ab}Fabt​, and the Schur basis carries no orthogonality relation. A formalization that builds (86) or (21) into the definitions makes Theorem 9 a tautology and is not this mission.
  • Source slips. Eq. (11) prints σab(n)=a\sigma_{ab}(n)=aσab​(n)=a, σab−1[n]=b\sigma^{-1}_{ab}[n]=bσab−1​[n]=b, contrary to Notation 1 and eq. (7); the Lean statements follow Notation 1 (σab(a)=n\sigma_{ab}(a)=nσab​(a)=n, σab(n)=b\sigma_{ab}(n)=bσab​(n)=b). Eq. (139) is stated with 1/∣G∣1/|G|1/∣G∣ and a sum over ggg, correcting the printed 1/n!1/n!1/n! and ∑h\sum_h∑h​.
  • Welcome contributions: the coordinate Schur orthogonality relations for matrix representations of finite groups, and lemmas on ptLast and on liftPerm. Both are reusable beyond this paper.

Selected references

  • M. Studziński, M. Horodecki, M. Mozrzymas, Commutant structuture of U⊗(n−1)⊗U∗U^{\otimes(n-1)}\otimes U^*U⊗(n−1)⊗U∗ transformations, arXiv:1305.6183v2, 2013. https://arxiv.org/abs/1305.6183
  • S. Ishizaka, T. Hiroshima, Asymptotic teleportation scheme as a universal programmable quantum processor, Phys. Rev. Lett. 101, 240501, 2008. https://arxiv.org/abs/0807.4568
  • M. Studziński, S. Strelchuk, M. Mozrzymas, M. Horodecki, Port-based teleportation in arbitrary dimension, Sci. Rep. 7, 10871, 2017. https://arxiv.org/abs/1612.09260
  • M. Mozrzymas, M. Studziński, M. Horodecki, A simplified formalism of the algebra of partially transposed permutation operators with applications, J. Phys. A 51, 125202, 2018. https://arxiv.org/abs/1708.02434
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Condensed Matter PhysicsMathematical PhysicsQuantum Mechanics+1·Captain: mikedeng1

Density Matrix Numerical Renormalization Group for Non-Abelian Symmetries: Tracing Out a Site Keeps the Reduced Density Matrix Symmetric and Gives the dim(ĩ)/dim(u) Recursion for Its Kept BlockResearch Paper

Motivation

Wilson's numerical renormalization group (NRG) computes the low-energy states of quantum impurity models, such as the Kondo and Anderson models, by diagonalizing a semi-infinite chain one site at a time and keeping only the lowest multiplets after each step (Wilson 1975; review: Bulla, Costi, Pruschke 2008). Dynamical quantities (spectral functions, Green's functions) at finite temperature require, in addition, the reduced density matrix of each shorter chain, obtained by tracing the full density matrix over the sites that come later. This is the density matrix NRG (DM-NRG) (Hofstetter 2000), made exact with the complete basis of Anders and Schiller 2005.

Symmetries make NRG practical: when the Hamiltonian is invariant under a group, every iteration splits into blocks labelled by irreducible representations, and each multiplet is stored once instead of once per internal state. For non-Abelian groups (SU(2) spin, SU(2) charge, SU(3) channel symmetry) the saving is large, but only if the reduced density matrix can also be handled multiplet by multiplet. Tóth, Moca, Legeza and Zaránd (arXiv:0802.4332) show that it can, for essentially any symmetry: the reduced density matrix stays scalar under the symmetry after each tracing step, and its reduced matrix elements obey an explicit recursion, their eq. (45), whose only group-theoretic input is the ratio of multiplet dimensions.

Setting

A group GGG acts on every multiplet through a labelled unitary irreducible representation: a label QQQ has a dimension dim⁡Q\dim QdimQ and unitary matrices πQ(g)\pi_Q(g)πQ​(g) of size dim⁡Q\dim QdimQ, and states of a multiplet are indexed by an internal quantum number Qz∈{0,…,dim⁡Q−1}Q^z\in\{0,\dots,\dim Q-1\}Qz∈{0,…,dimQ−1}. Distinct labels carry inequivalent representations.

At iteration nnn the new space is spanned by states ∣u,Quz;μ,qμz⟩|u, Q^z_u;\mu, q^z_\mu\rangle∣u,Quz​;μ,qμz​⟩, where u∈Ku\in Ku∈K runs over the kept multiplets of iteration n−1n-1n−1 (block states, label QuQ_uQu​) and μ∈Λ\mu\in\Lambdaμ∈Λ over the local multiplets of site nnn (label qμq_\muqμ​). The symmetry acts as

U(g)=V(g)⊗L(g),V(g)=⨁u∈KπQu(g),L(g)=⨁μ∈Λπqμ(g).U(g) = V(g)\otimes L(g),\qquad V(g)=\bigoplus_{u\in K}\pi_{Q_u}(g),\quad L(g)=\bigoplus_{\mu\in\Lambda}\pi_{q_\mu}(g).U(g)=V(g)⊗L(g),V(g)=u∈K⨁​πQu​​(g),L(g)=μ∈Λ⨁​πqμ​​(g).

Generalized Clebsch–Gordan coefficients ⟨q qz;Q Qz∣Q′ Q′z⟩\langle q\,q^z; Q\,Q^z\mid Q'\,Q'^z\rangle⟨qqz;QQz∣Q′Q′z⟩ are, for each label Q′Q'Q′ in a coupling set of (q,Q)(q,Q)(q,Q), the entries of a matrix CqQQ′C^{Q'}_{qQ}CqQQ′​ that is an isometry, intertwines πq⊗πQ\pi_q\otimes\pi_Qπq​⊗πQ​ with πQ′\pi_{Q'}πQ′​, and such that the CC†C C^\daggerCC† sum to the identity. The canonical basis (21) is

∣ı~,Qı~,Qı~z⟩=∑Quz,qμz⟨qμqμz;QuQuz∣Qı~Qı~z⟩ ∣u,Qu,Quz;μ,qμ,qμz⟩,|\tilde\imath, Q_{\tilde\imath}, Q^z_{\tilde\imath}\rangle = \sum_{Q^z_u, q^z_\mu}\langle q_\mu q^z_\mu; Q_u Q^z_u\mid Q_{\tilde\imath} Q^z_{\tilde\imath}\rangle\,|u, Q_u, Q^z_u;\mu, q_\mu, q^z_\mu\rangle,∣~,Q~​,Q~z​⟩=Quz​,qμz​∑​⟨qμ​qμz​;Qu​Quz​∣Q~​Q~z​⟩∣u,Qu​,Quz​;μ,qμ​,qμz​⟩,

one canonical multiplet ı~=((u,μ),Qı~)\tilde\imath = ((u,\mu),Q_{\tilde\imath})~=((u,μ),Q~​) per kept multiplet, local multiplet and coupled label; one writes ı~⇐u\tilde\imath\Leftarrow u~⇐u.

An operator RRR on the new space is scalar if U(g)RU(g)†=RU(g)RU(g)^\dagger = RU(g)RU(g)†=R for all ggg. Its reduced matrix elements ⟨ı~∥R~∥ȷ~⟩\langle\tilde\imath\Vert\tilde R\Vert\tilde\jmath\rangle⟨~∥R~∥~​⟩ in the canonical basis are defined by ⟨ı~,Qz∣R∣ȷ~,Qz′⟩=⟨ı~∥R~∥ȷ~⟩ δQı~,Qȷ~δQz,Qz′\langle\tilde\imath,Q^z\mid R\mid\tilde\jmath,Q^{z\prime}\rangle = \langle\tilde\imath\Vert\tilde R\Vert\tilde\jmath\rangle\,\delta_{Q_{\tilde\imath},Q_{\tilde\jmath}}\delta_{Q^z,Q^{z\prime}}⟨~,Qz∣R∣~​,Qz′⟩=⟨~∥R~∥~​⟩δQ~​,Q~​​​δQz,Qz′​. Tr⁡site\operatorname{Tr}_{\mathrm{site}}Trsite​ is the partial trace over the local states of site nnn.

Formalization targets

Goal: one tracing step (§III, eqs. (42)–(45))

For every scalar RRR: Tr⁡siteR\operatorname{Tr}_{\mathrm{site}}RTrsite​R is scalar under V(g)V(g)V(g); RRR has reduced matrix elements in the canonical basis; for kept multiplets u,vu,vu,v with Qu=QvQ_u=Q_vQu​=Qv​ and every such family of reduced elements,

⟨u,Qz∣Tr⁡siteR∣v,Qz′⟩=δQz,Qz′∑μ ∑ı~⇐(u,μ) ∑ȷ~⇐(v,μ)dim⁡(ı~)dim⁡(u) ⟨ı~∥R~∥ȷ~⟩ δQı~,Qȷ~;\langle u, Q^z\mid\operatorname{Tr}_{\mathrm{site}}R\mid v, Q^{z\prime}\rangle = \delta_{Q^z,Q^{z\prime}}\sum_{\mu}\ \sum_{\tilde\imath\Leftarrow(u,\mu)}\ \sum_{\tilde\jmath\Leftarrow(v,\mu)}\frac{\dim(\tilde\imath)}{\dim(u)}\,\langle\tilde\imath\Vert\tilde R\Vert\tilde\jmath\rangle\,\delta_{Q_{\tilde\imath},Q_{\tilde\jmath}};⟨u,Qz∣Trsite​R∣v,Qz′⟩=δQz,Qz′​μ∑​ ~⇐(u,μ)∑​ ~​⇐(v,μ)∑​dim(u)dim(~)​⟨~∥R~∥~​⟩δQ~​,Q~​​​;

and the entry vanishes when Qu≠QvQ_u\neq Q_vQu​=Qv​. The paper's truncated reduced density matrix R[n]R^{[n]}R[n] of its eq. (38) is one instance of RRR; the statement fixes no Hamiltonian, energies or temperature.

Milestones

In proof order: the covariance of the partial trace (40); the invariance of Tr⁡siteR\operatorname{Tr}_{\mathrm{site}}RTrsite​R and of R[n−1]R^{[n-1]}R[n−1] (39), (41)–(42); the Wigner–Eckart form of a scalar operator (43); orthonormality, completeness and the transformation law of the canonical basis (21); the Wigner–Eckart form in the canonical basis (44); and the Clebsch–Gordan sum rule that produces the factor dim⁡Q′/dim⁡Q\dim Q'/\dim QdimQ′/dimQ when the local states are traced out.

Significance

The recursion (45) computes the reduced density matrix of every iteration from that of the next, one number per pair of multiplets, so that all DM-NRG expectation values and spectral functions can be evaluated in the reduced (multiplet) representation. Its only group-dependent input is the dimension ratio dim⁡(ı~)/dim⁡(u)\dim(\tilde\imath)/\dim(u)dim(~)/dim(u); the Clebsch–Gordan coefficients themselves drop out. This is what lets a DM-NRG code, such as the flexible code the paper describes, handle any number and type of symmetries without symmetry-specific density-matrix code.

The result is proved in the paper, in a few lines of physics notation that rely on the Wigner–Eckart theorem and on Clebsch–Gordan orthogonality. It has no machine-checked proof. The mission states it as a theorem about invariant matrices, unitary representations and Clebsch–Gordan isometries; its milestones include a Wigner–Eckart theorem for scalar operators on a space of labelled multiplets and a Clebsch–Gordan sum rule, both stated for an arbitrary group and reusable for other symmetry-adapted tensor-network and many-body computations.

Difficulty

The physics proof cites the Wigner–Eckart theorem; formally this is Schur's lemma in two forms: an equivariant map between inequivalent irreducible representations vanishes, and an equivariant endomorphism of an irreducible representation is a scalar. Neither is a hypothesis here, so both must be derived from irreducibility in its invariant-subspace form. The dimension factor is the second form applied to the partial trace of CC†CC^\daggerCC†, together with a trace count. A direct computation from the definition of the canonical basis does not give (45): the Clebsch–Gordan coefficients are arbitrary matrices subject only to the three stated conditions, so no closed formula for them is available, and the dimension ratio appears only through the representation-theoretic argument. The bookkeeping is also non-trivial: internal quantum numbers of different multiplets live in different index types even when their labels coincide, and the tilde sum pairs canonical multiplets built with the same local multiplet.

Formalization scope

One abstract group GGG with labelled irreducible unitary representations replaces the paper's product G1×⋯×GΓG_1\times\dots\times G_\GammaG1​×⋯×GΓ​ of eqs. (17)–(18); the product case is an instance, with the generalized Clebsch–Gordan coefficients of p. 4 as products. Irreducibility is Mathlib's Representation.IsIrreducible, inequivalence the emptiness of Representation.Equiv. The coupling is multiplicity-free, one Clebsch–Gordan matrix per label triple, exactly as (21) is written; with outer multiplicities (as in SU(3)) the printed (45) would need a multiplicity index. Operators on the new space are matrices indexed by (block, local) pairs, block first, and the partial trace over the site is the published WildeQIT.partialTraceRight; Clebsch–Gordan matrices are indexed local first. All index sets are finite. Internal quantum numbers of multiplets with equal labels are compared by their numerical value. No fermionic sign factors appear: the space is the tensor product in the ordering of (21), and (45) carries no sign.

The statement does not assume Schur's lemma, the Clebsch–Gordan sum rule, or a block form of RRR; the reduced matrix elements in (45) are not free parameters but any family satisfying the Wigner–Eckart identity for RRR, whose existence is part of the goal. The Wilson chain, the complete basis and density matrix (32)–(38), the Green's function (46)–(48), the numerics of §V and the SU(2) appendix are outside the mission.

A complete development needs a usable Schur's lemma for matrix representations (Mathlib provides the module-theoretic version), Kronecker-product and block-diagonal matrix algebra, and partial-trace identities. Lemmas on partial traces of Kronecker products and on Wigner–Eckart for block-diagonal representations are reusable beyond this mission; contributions of either kind are welcome.

Selected references

  • A. I. Tóth, C. P. Moca, Ö. Legeza, G. Zaránd, Density matrix numerical renormalization group for non-Abelian symmetries, arXiv:0802.4332v1, 2008. https://arxiv.org/abs/0802.4332
  • K. G. Wilson, The renormalization group: Critical phenomena and the Kondo problem, Reviews of Modern Physics 47, 773, 1975. https://doi.org/10.1103/RevModPhys.47.773
  • W. Hofstetter, Generalized numerical renormalization group for dynamical quantities, Physical Review Letters 85, 1508, 2000. https://doi.org/10.1103/PhysRevLett.85.1508
  • F. B. Anders, A. Schiller, Real-time dynamics in quantum-impurity systems: a time-dependent numerical renormalization-group approach, Physical Review Letters 95, 196801, 2005. https://doi.org/10.1103/PhysRevLett.95.196801
  • R. Bulla, T. A. Costi, T. Pruschke, Numerical renormalization group method for quantum impurity systems, Reviews of Modern Physics 80, 395, 2008. https://arxiv.org/abs/cond-mat/0611651
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Mathematical PhysicsRepresentation Theory·Captain: mikedeng1

Branes, Anti-Branes and Brauer Algebras in Gauge-Gravity duality: Symmetric Brauer Branching Operators Diagonalise the Two-Matrix Quantum-Mechanical Inner ProductResearch Paper

Motivation

Operators made from a complex matrix and its conjugate occur in the non-chiral sector of gauge theories. Their correlation functions involve many possible pairings of matrix entries, and those pairings become difficult to organize as the number of entries grows. Kimura and Ramgoolam use the walled Brauer algebra to label these operators and to describe their two-point functions in a basis adapted to both the tensor symmetries and the mixed fundamental–conjugate representation. The result targeted here is the orthogonality relation for that basis in the reduced two-matrix quantum mechanics Kimura and Ramgoolam, §7.1, p. 29.

The question is concrete: can the algebraic labels of an operator determine whether its state has zero inner product with another state, without expanding every matrix entry? Equation (7.12) says yes for the paper's symmetric branching operators. It is a known result in the cited paper. This mission asks for a machine-checked formalization of its precise tensor-space content, including the complex Gaussian covariance and the permutation sums on which the two-point function depends.

Setting

Let V=CNV=\mathbb C^NV=CN, with basis indexed by Fin⁡N\operatorname{Fin}NFinN. The mixed tensor space V⊗m⊗Vˉ⊗nV^{\otimes m}\otimes\bar V^{\otimes n}V⊗m⊗Vˉ⊗n has a basis labelled by a pair of index tuples: an mmm-tuple for the VVV legs and an nnn-tuple for the conjugate legs. Its operators are finite complex matrices; tr⁡m,n\operatorname{tr}_{m,n}trm,n​ is their ordinary matrix trace. The group Sm×SnS_m\times S_nSm​×Sn​ permutes legs within each block. It is represented by matrices Pσ,τP_{\sigma,\tau}Pσ,τ​, using the already published tensor-permutation definition used here.

A contraction CijˉC_{i\bar j}Cijˉ​​ pairs the iiith VVV leg with the jjjth conjugate leg. The image of the algebra generated by all such contractions and the Sm×SnS_m\times S_nSm​×Sn​ permutations is denoted BN(m,n)B_N(m,n)BN​(m,n). This is the represented Brauer algebra used in the paper's tensor-space traces §3.2, pp. 8–9. The map Σ\SigmaΣ exchanges the input and output indices of the conjugate block. In coordinates it is the partial transpose on that block; this matches the reflection shown in Figure 6 §3.3, pp. 11–12.

The paper's symmetric branching operators are denoted QA,ijγQ^\gamma_{A,ij}QA,ijγ​. Here γ\gammaγ labels a Brauer representation, AAA an irreducible representation of Sm×SnS_m\times S_nSm​×Sn​, and i,ji,ji,j range over its multiplicity MAγM^\gamma_AMAγ​. These operators belong to the represented Brauer algebra, are invariant under conjugation by every Pσ,τP_{\sigma,\tau}Pσ,τ​, and obey the matrix-unit product QA,ijγ1QB,klγ2=δγ1γ2δABδjkQA,ilγ1Q^{\gamma_1}_{A,ij}Q^{\gamma_2}_{B,kl}=\delta_{\gamma_1\gamma_2}\delta_{AB}\delta_{jk}Q^{\gamma_1}_{A,il}QA,ijγ1​​QB,klγ2​​=δγ1​γ2​​δAB​δjk​QA,ilγ1​​. The relation (QA,ijγ)†=QA,jiγ(Q^\gamma_{A,ij})^\dagger=Q^\gamma_{A,ji}(QA,ijγ​)†=QA,jiγ​ comes from their orthonormal-vector definition in (7.3) §7, pp. 27–28.

For two independent N×NN\times NN×N complex matrices ZA,ZBZ_A,Z_BZA​,ZB​, put

fX(ZA,ZB)=tr⁡m,n ⁣(X(ZA⊗m⊗ZB⊗n)),⟨X2,X1⟩=E ⁣[fX2(ZA,ZB)‾fX1(ZA,ZB)].f_X(Z_A,Z_B)=\operatorname{tr}_{m,n}\!\left(X\left(Z_A^{\otimes m}\otimes Z_B^{\otimes n}\right)\right), \qquad \langle X_2,X_1\rangle=\mathbb E\!\left[\overline{f_{X_2}(Z_A,Z_B)}f_{X_1}(Z_A,Z_B)\right].fX​(ZA​,ZB​)=trm,n​(X(ZA⊗m​⊗ZB⊗n​)),⟨X2​,X1​⟩=E[fX2​​(ZA​,ZB​)​fX1​​(ZA​,ZB​)].

Each matrix entry has independent real and imaginary parts of variance 1/21/21/2. This Bargmann–Fock Gaussian realization gives E∣Zij∣2=1\mathbb E|Z_{ij}|^2=1E∣Zij​∣2=1 and reproduces the oscillator normalization of (7.8) p. 28.

Formalization targets

The goal is the orthogonality identity of (7.12), with the scalar written as a tensor-space trace:

⟨Σ(QA2,i2j2γ2),Σ(QA1,i1j1γ1)⟩=m!n! δγ1γ2δA1A2δi1i2δj1j2tr⁡m,n(QA1,j1j1γ1).\left\langle\Sigma(Q^{\gamma_2}_{A_2,i_2j_2}), \Sigma(Q^{\gamma_1}_{A_1,i_1j_1})\right\rangle =m!n!\,\delta_{\gamma_1\gamma_2}\delta_{A_1A_2} \delta_{i_1i_2}\delta_{j_1j_2} \operatorname{tr}_{m,n}(Q^{\gamma_1}_{A_1,j_1j_1}).⟨Σ(QA2​,i2​j2​γ2​​),Σ(QA1​,i1​j1​γ1​​)⟩=m!n!δγ1​γ2​​δA1​A2​​δi1​i2​​δj1​j2​​trm,n​(QA1​,j1​j1​γ1​​).

The paper writes the last factor as dA1Dim⁡γ1d_{A_1}\operatorname{Dim}\gamma_1dA1​​Dimγ1​ after invoking mixed-tensor Schur–Weyl duality. The trace version is the equality reached just before that last step in (7.13) p. 29. The mission's milestones cover the unit covariance (7.8), the Wick permutation formula of Figure 3, the trace sum in the second line of (7.13), symmetry preservation (3.37), the m!n!m!n!m!n! collapse in the third line of (7.13), and the trace-pairing identity (3.20).

Significance

The formula assigns orthogonal states to different branching labels and multiplicity indices. It also fixes their norms by an explicit finite-dimensional trace. This makes the representation labels useful for organizing non-chiral two-point functions, rather than merely counting or naming operators §7, pp. 27–29. Without the Wick-to-trace identity, the same claim remains an expanding sum of matrix-entry pairings for each degree.

The paper proves the identity diagrammatically; this mission has no machine-checked proof of it yet. A completed formalization would give reusable finite-matrix interfaces for contractions, partial transpose, Gaussian polynomial states, and permutation-indexed Wick sums. The complex covariance and Wick results can be reused independently of the particular Brauer family. The represented algebra and branching interface can support later formalization of the paper's projectors and finite-NNN examples.

Difficulty

The central obstacle is the passage from a Gaussian expectation of products of matrix entries to a sum of traces indexed by Sm×SnS_m\times S_nSm​×Sn​. A direct expansion of the state polynomials creates many indices, and each Gaussian pairing must match the direction of the matrix entries and the inverse permutation in the tensor action. The AAA and BBB matrices are independent, so pairings cannot cross their blocks. After that conversion, the partial transpose and permutation symmetry must agree on the same index convention. A transposed covariance or reversed inverse produces a plausible looking expression with the wrong contractions.

Formalization scope

The Lean model uses finite matrices throughout, permits N,m,n=0N,m,n=0N,m,n=0, and uses ordinary unnormalized traces. The index type is a pair of functions from Fin⁡m\operatorname{Fin}mFinm and Fin⁡n\operatorname{Fin}nFinn to Fin⁡N\operatorname{Fin}NFinN; its second component represents the conjugate legs. Σ\SigmaΣ is the concrete partial transpose, and BN(m,n)B_N(m,n)BN​(m,n) is the image subalgebra generated inside the matrix algebra. The oscillator vacuum inner product is the Gaussian integral above, with two independent matrices and unit complex variance. It is defined from the measure, not from the Wick permutation formula. The measure's pushforward map is continuous. Matrix-entry polynomials have finite Gaussian moments, so the integral does not rely on Lean's default value for a non-integrable function.

The branching labels are abstract types, but each valid QA,ijγQ^\gamma_{A,ij}QA,ijγ​ must satisfy Brauer membership, the paper's symmetry and matrix-unit relations, and the adjoint relation derived from (7.3). Indices are natural numbers with explicit range conditions i,j<MAγi,j<M^\gamma_Ai,j<MAγ​. No completeness axiom, positivity condition, or lower bound on NNN is added. These declarations exclude a trivialized inner product defined as the target permutation sum. The coefficient dADim⁡γd_A\operatorname{Dim}\gammadA​Dimγ and the four-dimensional, normal-ordered version (7.16) are outside this mission. Contributions to the Gaussian Wick conversion, the partial-transpose trace identity, and the symmetric branching calculation are all in scope.

Selected references

  • Y. Kimura and S. Ramgoolam, Branes, Anti-Branes and Brauer Algebras in Gauge-Gravity duality, JHEP 11 (2007) 078; arXiv:0709.2158v2. The mission cites the version 2 preprint's printed page and equation numbers.
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Linear algebraQuantum Information·Captain: mikedeng1

On a Family of a Linear Maps from Mₙ(ℂ) to Mₙ²(ℂ): Φ[α, β] on M₃(ℂ) Is 2-Positive and Not Completely Positive Exactly When β ≤ −0.2 and α Lies in an Explicit IntervalResearch Paper

Motivation

Positive linear maps on complex matrix algebras provide tests for quantum entanglement: applying such a map to one part of a bipartite state can expose a state that cannot be written as a mixture of product states. The distinction between ordinary positivity, positivity with an auxiliary matrix system, and complete positivity is therefore substantive. Collins, Osaka, and Sapra study an explicit two-parameter family that makes these distinctions visible through inequalities in the parameters. Their paper identifies the exact region where a map from three-by-three matrices to nine-by-nine matrices is 2-positive but fails to be completely positive. This is the stated main result of their 2018 paper, rather than an open conjecture.

Setting

Write Mn(C)M_n(\mathbb C)Mn​(C) for the algebra of n×nn\times nn×n complex matrices. A matrix XXX is positive semidefinite, written X≥0X\geq0X≥0, when it is Hermitian and has no negative eigenvalues. A complex-linear map Ψ:Md1(C)→Md2(C)\Psi:M_{d_1}(\mathbb C)\to M_{d_2}(\mathbb C)Ψ:Md1​​(C)→Md2​​(C) is positive when X≥0X\geq0X≥0 implies Ψ(X)≥0\Psi(X)\geq0Ψ(X)≥0. It is kkk-positive when the same condition holds after the identity map on Mk(C)M_k(\mathbb C)Mk​(C) is tensored with Ψ\PsiΨ. It is completely positive when it is kkk-positive for every kkk. A map is completely copositive when its composition with transpose on the source is completely positive. These are the paper's definitions on page 2.

Let Bn=∑i=1nei⊗eiB_n=\sum_{i=1}^n e_i\otimes e_iBn​=∑i=1n​ei​⊗ei​ be the unnormalized Bell vector, and let Bn2=BnBn∗B_{n^2}=B_nB_n^*Bn2​=Bn​Bn∗​ denote its rank-one projector. For real α,β\alpha,\betaα,β and n≥3n\geq3n≥3, the family on page 3 is

Φα,β,n(A)=AT⊗In+In⊗A+Tr⁡(A)(αIn2+βBn2).\Phi_{\alpha,\beta,n}(A) =A^{\mathsf T}\otimes I_n+I_n\otimes A +\operatorname{Tr}(A)\bigl(\alpha I_{n^2}+\beta B_{n^2}\bigr).Φα,β,n​(A)=AT⊗In​+In​⊗A+Tr(A)(αIn2​+βBn2​).

The transpose belongs to the first tensor factor. The mission concentrates on Φ[α,β]=Φα,β,3\Phi[\alpha,\beta]=\Phi_{\alpha,\beta,3}Φ[α,β]=Φα,β,3​. Its Choi matrix is CΨ=∑i,jeij⊗Ψ(eij)C_\Psi=\sum_{i,j}e_{ij}\otimes\Psi(e_{ij})CΨ​=∑i,j​eij​⊗Ψ(eij​). For this finite-dimensional setting, the Choi criterion relates complete positivity to CΨ≥0C_\Psi\geq0CΨ​≥0; it does not define complete positivity. The paper also identifies an equivariance property: unitary conjugation of the input corresponds to conjugation of the output by a matrix associated with the same unitary.

Formalization targets

For β∈R\beta\in\mathbb Rβ∈R, define two boundary curves and a cubic root:

ℓP(β)=−(2+3β)+9β2−4β+42,ℓCP(β)=−(3+3β)+9β2−10β+172,\ell_P(\beta)=\frac{-(2+3\beta)+\sqrt{9\beta^2-4\beta+4}}2, \qquad \ell_{CP}(\beta)=\frac{-(3+3\beta)+\sqrt{9\beta^2-10\beta+17}}2,ℓP​(β)=2−(2+3β)+9β2−4β+4​​,ℓCP​(β)=2−(3+3β)+9β2−10β+17​​, Sβ=min⁡{x∈R:x3+(−2−3β)x2+(−2+4β)x+2β=0}.S_\beta=\min\{x\in\mathbb R:x^3+(-2-3\beta)x^2+(-2+4\beta)x+2\beta=0\}.Sβ​=min{x∈R:x3+(−2−3β)x2+(−2+4β)x+2β=0}.

The goal is Theorem 3.9, page 10:

Φ[α,β] is 2-positive and not completely positive⟺β≤−15,α∈[ℓP(β),ℓCP(β))∩[1,∞)∩[−Sβ,∞).\Phi[\alpha,\beta]\text{ is 2-positive and not completely positive} \quad\Longleftrightarrow\quad \beta\leq-\tfrac15, \quad\alpha\in[\ell_P(\beta),\ell_{CP}(\beta)) \cap[1,\infty)\cap[-S_\beta,\infty).Φ[α,β] is 2-positive and not completely positive⟺β≤−51​,α∈[ℓP​(β),ℓCP​(β))∩[1,∞)∩[−Sβ​,∞).

The left endpoint of the first interval is included and the right endpoint is excluded. The milestones record the paper's earlier criteria for positivity, complete positivity, and 2-positivity, together with its general equivariance criterion and the computation for this family. Their inequalities are separate targets; none is built into the definitions of these map properties.

Significance

The goal gives an exact, parameter-level example of 2-positivity without complete positivity. It identifies all such members of this family on M3(C)M_3(\mathbb C)M3​(C), including the boundary cases, rather than only exhibiting one map. The surrounding results locate the positive and completely positive regions and give a criterion for testing kkk-positivity of equivariant maps. Together, they provide a compact source of concrete maps with different strengths of positivity, as described in Sections 2 and 3 of the paper.

The mathematical result was proved in 2018. This mission seeks a machine-checked account of the stated criteria, with the family, Choi matrix, Bell projector, and positivity predicates exposed as reusable declarations. A finished proof would connect elementary matrix positivity and tensor products to explicit real-algebraic parameter bounds. It would also make each boundary independently inspectable. The Lean theorem statements here are draft targets with sorry; they are not existing machine-checked proofs of the paper.

Difficulty

Testing only Φ[α,β](e11)≥0\Phi[\alpha,\beta](e_{11})\geq0Φ[α,β](e11​)≥0 yields ordinary positivity, not 2-positivity. Testing only the Choi matrix addresses complete positivity, which is stronger than the desired property. The 2-positive condition quantifies over every positive semidefinite matrix in M2(C)⊗M3(C)M_2(\mathbb C)\otimes M_3(\mathbb C)M2​(C)⊗M3​(C); the paper's criterion reduces that universal condition under equivariance to positivity of a specific block matrix. The block matrix has parameter-dependent eigenvalues, including roots of a cubic. This makes a simple comparison of the two square-root boundary curves insufficient: the smallest cubic root supplies an additional lower bound on α\alphaα. The endpoint β=−1/5\beta=-1/5β=−1/5 also needs attention because the case descriptions for complete positivity meet there.

Formalization scope

The Lean model uses Matrix (Fin n) (Fin n) ℂ for Mn(C)M_n(\mathbb C)Mn​(C) and Fin n × Fin n for the output tensor indices, with the first coordinate corresponding to the transposed factor. The paper's one-based e11e_{11}e11​ is Matrix.single 0 0 1. Positive semidefiniteness is Mathlib's Matrix.PosSemidef. The shared QAlgorithms.idTensorSuperop applies a map blockwise with the auxiliary index first, and the published QAlgorithms.Entanglement.IsCompletelyPositive quantifies over all auxiliary sizes. Size zero is harmless for that quantifier because the corresponding matrix type is empty. The paper's n≥3n\geq3n≥3 appears on the general-family results; the main goal fixes n=3n=3n=3.

The Bell projector is unnormalized, matching the paper's BnBn∗B_nB_n^*Bn​Bn∗​. The two square roots have positive radicands on the real parameter range. The cubic root SβS_\betaSβ​ is defined as the infimum of its real root set; the odd-degree cubic has a nonempty, finite set of real roots, so this is its least root. The proof development needs reusable facts about unitary conjugation of positive matrices, Kronecker products, Choi matrices, and positivity of the parameterized block matrix. Direct calculations of the boundary inequalities are also welcome. Defining kkk-positivity by the block criterion, complete positivity by Choi positivity, omitting the transpose in Φ\PhiΦ, or treating SβS_\betaSβ​ as an arbitrary parameter would make the targets lose their intended content.

Selected references

  • Benoît Collins, Hiroyuki Osaka, and Gunjan Sapra, On a family of a linear maps from Mn(C) to Mn2(C), Linear Algebra and its Applications, 2018; cited here from arXiv:1802.07553v1, pp. 2–10.
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Machine LearningProbability·Captain: mikedeng1

Speculative Actions: A Lossless Framework for Faster Agentic Systems: Exact Expected Runtime Ratio of k-Branch Speculation Under Exponential LatenciesResearch Paper

Motivation

An agentic system may make several slow API calls in sequence: the answer to one call determines what to request next. A speculator can guess the current answer while the real call is running and start likely next calls early. When a guess is correct, the system can reuse a pending result; when it is wrong, it discards that result. This can shorten a run if concurrent calls do not interfere and incorrect calls have no lasting side effects. Ye and coauthors introduce this execution scheme for language-model and tool-using agents in Speculative Actions, §2.

The paper's Proposition 1 quantifies the expected latency of one-step speculation with exponential call times. Its proof in Appendix A states a stochastic timing model and computes a finite-horizon runtime ratio and its limit. The first author's existing Prove2Me mission, Speculative Actions: Cost-Latency Analysis for Agentic Speculation, contains proved algebraic identities for expressions named specTime and seqTime. Those expressions encode the values claimed for the expectations. This mission supplies the missing connection between random executions and those expressions, while reusing the published algebra.

Setting

There are TTT real API calls, numbered 000 through T−1T-1T−1. The real-call latency of step ttt is BtB_tBt​ with exponential rate β\betaβ. At a speculation window during step ttt, a speculator has latency AtA_tAt​ with exponential rate α\alphaα. It produces kkk candidate next calls. Each candidate independently matches the actual next call with probability ppp, so a hit means that at least one of the kkk candidates matches. The hit probability is

p(k)=1−(1−p)k.p(k)=1-(1-p)^k.p(k)=1−(1−p)k.

All latencies and branch-correctness indicators are mutually independent. Proposition 1 assumes 0<β<α0<\beta<\alpha0<β<α and Assumption 1 gives p>0p>0p>0; as a probability, p≤1p\le1p≤1. Assumption 2 permits concurrent API calls and reversible or side-effect-free pre-launching. The theorem also treats state transitions and construction of API parameters as having negligible duration. These conditions and the exponential laws are stated in §2 and Proposition 1.

The sequential runtime TseqT_{\mathrm{seq}}Tseq​ is ∑t<TBt\sum_{t<T}B_t∑t<T​Bt​. The speculative runtime TsT_sTs​ follows Appendix A's timing rule. At a window without a hit, the current step costs BtB_tBt​. At a hit, the block comprising steps ttt and t+1t+1t+1 costs min⁡(At,Bt)+Bt+1\min(A_t,B_t)+B_{t+1}min(At​,Bt​)+Bt+1​, and step t+1t+1t+1 opens no window. The last step cannot open a window. This is the model used in the paper's proof of Proposition 1, Appendix A, pp. 14–15. Its precise timing rule matters: reading Algorithm 1's awaits literally gives a different timeline, as does waiting for validation before accepting an early next-step result.

Let SnS_nSn​ be the expected number of hits in nnn potential windows, with a hit consuming the following window. The paper gives S0=0S_0=0S0​=0, S1=p(k)S_1=p(k)S1​=p(k), and

Sn=p(k)(1+Sn−2)+(1−p(k))Sn−1(n≥2).S_n=p(k)(1+S_{n-2})+(1-p(k))S_{n-1}\qquad(n\ge2).Sn​=p(k)(1+Sn−2​)+(1−p(k))Sn−1​(n≥2).

Formalization targets

Random-runtime identities

The new milestones identify the expectation of the sequential runtime, the saving from one correct speculation, and the expected hit count. The total-saving identity is

E[Ts]=Tβ−ST−1αβ(α+β),T≥1.\mathbb E[T_s]=\frac{T}{\beta}-S_{T-1}\frac{\alpha}{\beta(\alpha+\beta)},\qquad T\ge1.E[Ts​]=βT​−ST−1​β(α+β)α​,T≥1.

Here ST−1S_{T-1}ST−1​ is the expectation of the random count defined by the execution model. The previously published expression SpecActions.specTime has this same algebraic form; proving equality with the integral of the random runtime is the new step. The closed form for SnS_nSn​ and the algebraic ratio are linked as published milestones.

Proposition 1

For every integer T≥1T\ge1T≥1, the goal is the Proposition 1 ratio for the actual expectations:

E[Ts]E[Tseq]=1−1Tαα+β[(T−1)p(k)1+p(k)+p(k)2(1+p(k))2−p(k)2(1+p(k))2(−p(k))T−1].\frac{\mathbb E[T_s]}{\mathbb E[T_{\mathrm{seq}}]}=1-\frac1T\frac{\alpha}{\alpha+\beta}\left[\frac{(T-1)p(k)}{1+p(k)}+\frac{p(k)^2}{(1+p(k))^2}-\frac{p(k)^2}{(1+p(k))^2}(-p(k))^{T-1}\right].E[Tseq​]E[Ts​]​=1−T1​α+βα​[1+p(k)(T−1)p(k)​+(1+p(k))2p(k)2​−(1+p(k))2p(k)2​(−p(k))T−1].

The same goal states convergence of this random-runtime ratio as T→∞T\to\inftyT→∞ to

1−p(k)1+p(k)αα+β.1-\frac{p(k)}{1+p(k)}\frac{\alpha}{\alpha+\beta}.1−1+p(k)p(k)​α+βα​.

The horizon condition protects the denominator: at T=0T=0T=0, the sequential runtime is zero. The number of branches may be zero; then p(0)=0p(0)=0p(0)=0 and the ratio is one for positive horizons.

Significance

The formula describes how breadth kkk, branch accuracy ppp, and the two latency rates affect expected completion time, both for a finite sequence of calls and in a long run. It makes the expected benefit of extra branches a quantity that can be compared with their resource cost. The paper uses the time expression again in its cost–latency discussion, including Theorem 5, p. 20.

The author's existing mission has machine-checked proofs for the recurrence's closed form and for algebraic identities involving specTime and seqTime. It does not prove that those expressions are expectations of a random execution. Completing this mission adds that probabilistic interpretation and a reusable model of independent calls, branch indicators, skipped windows, and runtime. The proposed stochastic theorems remain open; their statements compile, while the published reference results are already proved.

Difficulty

The main issue is the dependence created by skipping a window after a hit. Hit indicators at different windows are independent, but the number of opportunities actually used is determined by earlier outcomes. Treating the hit count as an ordinary binomial count gives the wrong recurrence. Likewise, multiplying a mean saving by the number of potential windows ignores that skipped windows cannot hit. A formal proof must relate the random execution and hit count while preserving the independence between correctness indicators and latencies.

Formalization scope

Lean represents all primitive random variables on one probability space. An index type separates speculator latency, real-call latency, and each of the kkk branch indicators at every window. IsSpecModel requires measurable variables, their exponential or real-valued Bernoulli laws, and mutual independence. A hit is an existential statement over the kkk branch indicators. The runtimes and hit count are recursions on a finite horizon, and expectation is the real Bochner integral. The model has an explicit construction on a product probability space, so its hypotheses are satisfiable.

Windows start at index zero: a TTT-step run has T−1T-1T−1 potential windows. The theorem keeps the paper's β<α\beta<\alphaβ<α and p>0p>0p>0 conditions, and specifies p≤1p\le1p≤1. It adds T≥1T\ge1T≥1 wherever a runtime ratio is divided by its sequential denominator. Concurrency without interference and negligible non-call work are represented by the timing recurrence. The claim concerns integrals of those random runtimes; defining an “expected runtime” directly by the paper's closed form would omit the target of this mission.

The definition layer and hit-probability, baseline, saving, hit-count, and total-saving milestones are available as separate contributions. Mathlib supplies exponential probability measures, product measures, independence, and integration; the published SpecActions definition and algebraic theorems are referenced. Work on the probabilistic bridges, integrability, and the final composition is within scope.

Selected references

  • N. Ye, A. Ahuja, G. Liargkovas, Y. Lu, K. Kaffes, and T. Peng, Speculative Actions: A Lossless Framework for Faster Agentic Systems, arXiv preprint arXiv:2510.04371v2, 2026. arXiv
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EconomicsOperations ResearchOptimization·Captain: mikedeng1

A Model of Product Line Marketing: When the Transformed Marketing Cost Is Concave, the Optimal Marketing Effort Allocation Concentrates on a Single Flagship ProductResearch Paper

Why product lines can have a flagship

Firms with several products often put most of their marketing behind one. The resulting product can become the line's flagship even when another product has a higher unit markup. He, Ke, and Zhang give a supply-side model in which this allocation follows from the relation between consumer taste dispersion and the cost of raising a product's perceived utility. Their main proposition says that a concave transformed marketing cost favors concentrating effort on one product. This mission formalizes that proposition and the fixed-effort claims that support it. He, Ke, and Zhang, A Model of Product Line Marketing, 2021 SSRN version, §2 and Online Appendix C.

The question is operational: a firm chooses marketing effort across its product line while sales move through a multinomial logit choice model. Spending on one product changes its share and also takes sales from the firm's other products. Concentrating marketing effort therefore cannot be inferred from looking at a single product's markup in isolation. The paper's marketing effort allocation index provides a way to compare products at a given total effort. He, Ke, and Zhang, §2.1, pp. 12–15.

The monopoly model

A firm sells nnn products. Product iii has base utility ui∈Ru_i\in\mathbb Rui​∈R and markup πi>0\pi_i>0πi​>0. Consumers either buy one product or select an outside option with utility normalized to zero. A Gumbel taste shock has scale λ>0\lambda>0λ>0. The firm raises product iii's utility by Δui≥θ\Delta u_i\geq\thetaΔui​≥θ, where θ∈R\theta\in\mathbb Rθ∈R is the common lower bound, and pays c(Δui)c(\Delta u_i)c(Δui​). The paper assumes the cost ccc is strictly increasing, convex, and continuously differentiable on feasible utility increases. The logit choice formula gives product iii a share proportional to e(ui+Δui)/λe^{(u_i+\Delta u_i)/\lambda}e(ui​+Δui​)/λ, with 111 in the denominator for the outside option. He, Ke, and Zhang, §2, pp. 7–9.

Equation (7) uses a nonnegative effort multiplier mi=eΔui/λ−eθ/λm_i=e^{\Delta u_i/\lambda}-e^{\theta/\lambda}mi​=eΔui​/λ−eθ/λ. A marketing effort allocation, or MEA strategy, is the vector m=(mi)m=(m_i)m=(mi​) with every mi≥0m_i\geq0mi​≥0. Its total marketing effort, or TME, is M=∑imiM=\sum_i m_iM=∑i​mi​. In these coordinates the firm's program (8) maximizes

P(m)=∑i(mi+eθ/λ)eui/λπi1+∑i(mi+eθ/λ)eui/λ−∑ic ⁣(λlog⁡(mi+eθ/λ))P(m)=\frac{\sum_i(m_i+e^{\theta/\lambda})e^{u_i/\lambda}\pi_i}{1+\sum_i(m_i+e^{\theta/\lambda})e^{u_i/\lambda}}-\sum_i c\!\left(\lambda\log(m_i+e^{\theta/\lambda})\right)P(m)=1+∑i​(mi​+eθ/λ)eui​/λ∑i​(mi​+eθ/λ)eui​/λπi​​−i∑​c(λlog(mi​+eθ/λ))

over every feasible mmm. The first term is revenue and the second is marketing cost. A strategy is concentrated in Definition 1 exactly when one multiplier is positive and all the others are zero. In particular, the zero allocation is a separate case. The transformed cost is fc(v)=c(λlog⁡v)f_c(v)=c(\lambda\log v)fc​(v)=c(λlogv). He, Ke, and Zhang, pp. 10–11, equations (7)–(8), Definition 1, Proposition 1.

For positive total effort MMM, product iii's MEA index is Ri(M)=Ai(M)/Bi(M)R_i(M)=A_i(M)/B_i(M)Ri​(M)=Ai​(M)/Bi​(M), where

Ai(M)=eui/λπi+1M∑je(uj+θ)/λπj,Bi(M)=eui/λ+1M∑je(uj+θ)/λ+1M.A_i(M)=e^{u_i/\lambda}\pi_i+\frac{1}{M}\sum_j e^{(u_j+\theta)/\lambda}\pi_j,\qquad B_i(M)=e^{u_i/\lambda}+\frac{1}{M}\sum_j e^{(u_j+\theta)/\lambda}+\frac{1}{M}.Ai​(M)=eui​/λπi​+M1​j∑​e(uj​+θ)/λπj​,Bi​(M)=eui​/λ+M1​j∑​e(uj​+θ)/λ+M1​.

It equals the revenue when all MMM is assigned to product iii. This is Definition 2 and the sentence immediately following it. He, Ke, and Zhang, p. 12, Definition 2, equation (9).

Formalization targets

The goal is Proposition 1 with its implicit edge cases made explicit. If fcf_cfc​ is concave on [eθ/λ,∞)[e^{\theta/\lambda},\infty)[eθ/λ,∞), an optimum exists, some optimum is concentrated or zero, and pairwise distinct products have no mixed optimum:

∃m∗∈arg max⁡mi≥0P(m),∃m∗ optimal with (m∗=0 or concentrated),\exists m^*\in\operatorname*{arg\,max}_{m_i\geq0}P(m),\qquad \exists m^*\text{ optimal with }(m^*=0\text{ or concentrated}),∃m∗∈mi​≥0argmax​P(m),∃m∗ optimal with (m∗=0 or concentrated), [(ui,πi)≠(uj,πj) for i≠j]⟹[∀m∗ optimal, m∗=0 or concentrated].\left[(u_i,\pi_i)\ne(u_j,\pi_j)\text{ for }i\ne j\right]\Longrightarrow\left[\forall m^*\text{ optimal},\ m^*=0\text{ or concentrated}\right].[(ui​,πi​)=(uj​,πj​) for i=j]⟹[∀m∗ optimal, m∗=0 or concentrated].

The milestone list follows the source's fixed-effort results. Lemma 3 bounds a weighted ratio by its largest component ratio and characterizes equality. The index interpretation and equation (C–9) connect that inequality to revenue. Lemma 4 concerns revenue at fixed TME, while Lemma 5 concerns cost at fixed TME. Proposition 2 restricts which products can receive positive effort at a profit optimum. Claim 1 and Lemma 6 address tied MEA indices at a positive optimal TME. He, Ke, and Zhang, Online Appendix C, pp. 40–43.

What the result establishes

The proposition identifies a class of marketing cost functions under which the firm can choose a single flagship product without giving up optimal profit. Under a distinct-product condition, it characterizes every optimum up to the possibility of zero total effort. The MEA index separately identifies which products can carry positive effort at an optimum. These statements let a solver study effort concentration and product selection within one common optimization model. He, Ke, and Zhang, Propositions 1–2, pp. 11 and 14–15.

The paper proves these results on paper. This mission asks for machine-checked proofs of the model-specific statements, including the edge cases left implicit in the printed wording. It also establishes reusable finite-sum facts, especially the ratio inequality and its equality condition. No machine-checked proof of this paper's marketing-effort theorem is claimed here.

The mathematical difficulty

A larger markup does not always make a product the best flagship. Revenue depends on every product's logit attraction and on the outside option, while cost depends on the entire allocation. Maximizing revenue at a fixed total effort therefore does not by itself solve the firm's original problem, whose total effort is also chosen. Ties in the MEA index create another difficulty: at a fixed total, several products can share effort without changing the revenue, so a concentration statement about every optimum needs a separate condition. He, Ke, and Zhang, §2.1 and Online Appendix C.

Formalization scope

Products are indexed by Fin n; the book's indices 1,…,n1,\ldots,n1,…,n are relabeled from zero. All monetary, utility, and multiplier quantities are real numbers. Strategies range over all nonnegative vectors, and optimality compares profit against every such vector. The outside-option attraction is 111. The cost assumptions apply on [θ,∞)[\theta,\infty)[θ,∞), and transformed-cost concavity applies on [eθ/λ,∞)[e^{\theta/\lambda},\infty)[eθ/λ,∞), the ranges reached by feasible allocations. The formula Ri(M)R_i(M)Ri​(M) is used only for M>0M>0M>0, so its 1/M1/M1/M terms never rely on Lean's default value for division by zero. Since mi+eθ/λ>0m_i+e^{\theta/\lambda}>0mi​+eθ/λ>0, every logarithm in the objective has a positive argument.

The goal includes existence of an optimum so that its statement about all optima has content. It distinguishes zero effort from Definition 1's concentrated allocation: zero effort can be optimal. Pairwise distinct (ui,πi)(u_i,\pi_i)(ui​,πi​) are required for the assertion about every optimum; identical products can support mixed optima. The paper says an optimum is “generically unique” but does not define a generic set or prove uniqueness of the optimal total effort under a specified genericity condition, so that phrase is outside this goal. Lemma 3 separately requires a positive total weight; Claim 1 restricts its rational curves to nonconstant ones. The printed proof of Lemma 6 has a gap for constant tied index curves, noted for reviewers. He, Ke, and Zhang, Definition 1, Proposition 1, Online Appendix C.

The development needs finite sums, real exponentials and logarithms, elementary calculus, and convexity and concavity on intervals. Its model definition is paper-specific; the ratio bound and rational-curve identification are useful beyond this setting. Contributions to any of the stated milestones, including their explicit boundary cases, are in scope.

Selected references

  • C. He, S. Ke, and X. Zhang, A Model of Product Line Marketing, Management Science, 2021; cited here from the August 2021 SSRN preprint, especially §2 and Online Appendix C. Journal DOI.
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Quantum Information·Captain: Goku

Norm-attaining tensors and nuclear operatorsResearch Paper

Motivation

Given two Banach spaces XXX and YYY, we introduce and study a concept of norm-attainment in the space of nuclear operators N(X,Y)\mathcal{N}(X,Y)N(X,Y) and in the projective tensor product space X⊗^πYX \widehat{\otimes}_\pi YX⊗​π​Y. We exhibit positive and negative examples where both previous norm-attainment hold. We also study the problem of whether the class of elements which attain their norms in N(X,Y)\mathcal{N}(X,Y)N(X,Y) and in X⊗^πYX\widehat{\otimes}_\pi YX⊗​π​Y is dense or not. We prove that, for both concepts, the density of norm-attaining elements holds for a large class of Banach spaces XXX and YYY which, in particular, covers all classical Banach spaces. Nevertheless, we present Banach spaces XXX and YYY failing the approximation property in such a way that the class of elements in X⊗^πYX\widehat{\otimes}_\pi YX⊗​π​Y which attain their projective norms is not dense. We also discuss some relations and applications of our work to the classical theory of norm-attaining operators throughout the paper.

This mission is the statement-level formalization of Norm-attaining tensors and nuclear operators (arXiv:2006.09871v4). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

44 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is Abstract main negative result, pinned to Theorem 5.1 (Abstract main negative result, pinned to Theorem 5.1, PDF p. 20). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Deferred results

3 results of the paper are not stated yet, because the library lacks an interface they need:

  • Example 4.12(d): absolute sums: Computed absolute normalized sum X ⊕a Y, with its normed and complete structures derived from a on R². (hard)
  • Remark 4.13: uniform crossnorm stability: A uniform family of reasonable crossnorms on all Banach pairs, its tensor-map metric mapping property, and the actual alpha completion. (hard)
  • Corollary 4.14: unrestricted final factor (source ambiguity): A source-supported hypothesis or proof for the unrestricted final factor Y. (source-ambiguity)

Selected references

  • Norm-attaining tensors and nuclear operators, arXiv:2006.09871v4, https://arxiv.org/abs/2006.09871v4
46 thms3 active usersReviewed
Quantum Information·Captain: Goku

Structure of states which satisfy strong subadditivity of quantum entropy with equalityResearch Paper

Motivation

We give an explicit characterisation of the quantum states which saturate the strong subadditivity inequality for the von Neumann entropy. By combining a result of Petz characterising the equality case for the monotonicity of relative entropy with a recent theorem by Koashi and Imoto, we show that such states will have the form of a so-called short quantum Markov chain, which in turn implies that two of the systems are independent conditioned on the third, in a physically meaningful sense. This characterisation simultaneously generalises known necessary and sufficient entropic conditions for quantum error correction as well as the conditions for the achievability of the Holevo bound on accessible information.

This mission is the statement-level formalization of Structure of states which satisfy strong subadditivity of quantum entropy with equality (arXiv:quant-ph/0304007v2). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

12 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is Theorem 6: structure of equality in strong subadditivity (original seed) (Theorem 6: structure of equality in strong subadditivity (original seed), PDF p. 4). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Deferred results

5 results of the paper are not stated yet, because the library lacks an interface they need:

  • Theorem 3 (Petz [15]): recovery equality and transpose formula: Finite-relative-entropy hypothesis and globally CPTP support completion (source-ambiguity)
  • Remark 5: cited near-optimal transpose-channel recovery: The fidelity/error objective, averaging convention, and reference ensemble for the transpose map (source-ambiguity)
  • Theorem 10 (Koashi–Imoto [7]): states and every invariant dilation: Support-qualified dilation-unitary block identity (source-ambiguity)
  • Proposition 11: equivalence of global dilation and channel block properties: Support-qualified equivalence between a channel property and a dilation property (source-ambiguity)
  • Section VII: claim that infinite A and C are covered while infinite B is open: Well-defined saturation for infinite outer registers and a completed-Hilbert structural interface (source-ambiguity)

Selected references

  • Structure of states which satisfy strong subadditivity of quantum entropy with equality, arXiv:quant-ph/0304007v2, https://arxiv.org/abs/quant-ph/0304007v2
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Quantum Information·Captain: Goku

Quantum conditional mutual information and approximate Markov chainsResearch Paper

Motivation

A state on a tripartite quantum system A⊗B⊗CA \otimes B \otimes CA⊗B⊗C forms a Markov chain if it can be reconstructed from its marginal on A⊗BA \otimes BA⊗B by a quantum operation from BBB to B⊗CB \otimes CB⊗C. We show that the quantum conditional mutual information I(A:C∣B)I(A: C | B)I(A:C∣B) of an arbitrary state is an upper bound on its distance to the closest reconstructed state. It thus quantifies how well the Markov chain property is approximated.

This mission is the statement-level formalization of Quantum conditional mutual information and approximate Markov chains (arXiv:1410.0664v3). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

39 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is Relative entropy from dominated approximants (Lemma 2.1, PDF p. 5). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Deferred results

6 results of the paper are not stated yet, because the library lacks an interface they need:

  • Lemma 3.1: A nonempty purification domain in the fixed ancillary space (source-ambiguity)
  • Lemma 4.1: A nonempty purification domain in the fixed ancillary space (source-ambiguity)
  • Lemma B.11: A nonempty purification domain in the fixed ancillary space (source-ambiguity)
  • Theorem 5.1: Arbitrary trace-class root fidelity and lower-semicontinuous extended CMI in a shared state model (hard)
  • Remark 5.2: Arbitrary trace-class root fidelity and lower-semicontinuous extended CMI in a shared state model (hard)
  • Eq.6: Arbitrary trace-class root fidelity and lower-semicontinuous extended CMI in a shared state model (hard)

Selected references

  • Quantum conditional mutual information and approximate Markov chains, arXiv:1410.0664v3, https://arxiv.org/abs/1410.0664v3
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Computational GeometryOperations ResearchOptimization·Captain: mikedeng1

Demand Equilibria in Spatial Service Systems: At Locally Optimal Service Rates the Decentralized Demand Equilibrium Coincides with the Socially Optimal PartitionResearch Paper

Motivation

Public and healthcare services — driver's licence offices, emergency rooms, testing sites — are spread over a region, and customers choose for themselves which facility to visit. A customer weighs two costs: the travel distance to a facility and the expected wait once there. The wait depends on how many other customers chose the same facility. The resulting division of the region is therefore an equilibrium rather than a geometric given, and it can differ from the division a central planner would impose. Deciding when selfish choices are socially efficient, and how a provider should spend a fixed service budget across facilities, is a basic question in spatial queueing and service-system design.

J. G. Carlsson, X. Peng and I. O. Ryzhov (SSRN 4794156, later published in Manufacturing & Service Operations Management, 2024) model each facility as an M/M/1 queue and show three things. The decentralized equilibrium is an additively weighted Voronoi diagram. Locally optimal centralized partitions are also weighted Voronoi diagrams, with weights given by a gradient. And if the service rates are chosen to minimize total social cost, the decentralized equilibrium is exactly the planner's partition. The last statement is Theorem 6 of the paper and the goal of this mission.

Setting

A closed convex region S⊆Rn\mathcal S \subseteq \mathbb R^nS⊆Rn carries a normalized demand ν\nuν: a probability measure on S\mathcal SS, absolutely continuous with respect to Lebesgue measure (the measure m(x) dxm(x)\,dxm(x)dx of the paper). Customers arrive at aggregate rate λ>0\lambda > 0λ>0. There are K≥1K \ge 1K≥1 facilities at points x1,…,xK∈Sx_1, \dots, x_K \in \mathcal Sx1​,…,xK​∈S. A continuous metric ddd gives travel distances dk(x)=d(x,xk)d_k(x) = d(x, x_k)dk​(x)=d(x,xk​). Facility kkk serves at rate μk\mu_kμk​.

A partition of S\mathcal SS is a family of measurable sets A1,…,AK⊆S\mathcal A_1, \dots, \mathcal A_K \subseteq \mathcal SA1​,…,AK​⊆S covering S\mathcal SS whose pairwise intersections are ν\nuν-null. The area of Ak\mathcal A_kAk​ is Ak=ν(Ak)A_k = \nu(\mathcal A_k)Ak​=ν(Ak​), the fraction of demand it holds. If facility kkk serves the fraction AkA_kAk​, its expected waiting cost, scaled by c>0c > 0c>0, is

fk(Ak)=cμk−λAk,λAk<μk.f_k(A_k) = \frac{c}{\mu_k - \lambda A_k}, \qquad \lambda A_k < \mu_k .fk​(Ak​)=μk​−λAk​c​,λAk​<μk​.

A decentralized demand equilibrium (1) under rates μ\muμ is a partition in which no customer prefers another facility:

dk(x)+fk(Ak)≤dj(x)+fj(Aj)for all x∈Ak, j≠k.d_k(x) + f_k(A_k) \le d_j(x) + f_j(A_j) \qquad \text{for all } x \in \mathcal A_k,\ j \ne k .dk​(x)+fk​(Ak​)≤dj​(x)+fj​(Aj​)for all x∈Ak​, j=k.

For weights w∈RKw \in \mathbb R^Kw∈RK, the weighted Voronoi cell of facility kkk is Ak(w)={x∈S:dk(x)+wk≤dj(x)+wj ∀j≠k}\mathcal A_k(w) = \{x \in \mathcal S : d_k(x) + w_k \le d_j(x) + w_j\ \forall j \ne k\}Ak​(w)={x∈S:dk​(x)+wk​≤dj​(x)+wj​ ∀j=k}. The equilibria are the diagrams whose weights solve wk=fk(Ak(w))w_k = f_k(A_k(w))wk​=fk​(Ak​(w)), condition (3).

The social cost (15) of rates μ\muμ and a partition is

∑kAk cμk−λAk+∑k∫Akdk dν,\sum_k A_k\,\frac{c}{\mu_k - \lambda A_k} + \sum_k \int_{\mathcal A_k} d_k\,d\nu,k∑​Ak​μk​−λAk​c​+k∑​∫Ak​​dk​dν,

minimized subject to the budget ∑kμk=B\sum_k \mu_k = B∑k​μk​=B, where B>λB > \lambdaB>λ.

A general partitioning problem (9)–(12) minimizes G(A1,…,AK)+∑k∫Akgk dνG(A_1, \dots, A_K) + \sum_k \int_{\mathcal A_k} g_k\,d\nuG(A1​,…,AK​)+∑k​∫Ak​​gk​dν over partitions. It carries a regularity condition: for j≠kj \ne kj=k, the law of gj(Y)−gk(Y)g_j(Y) - g_k(Y)gj​(Y)−gk​(Y) with Y∼νY \sim \nuY∼ν has a density.

Formalization targets

Goal (Theorem 6)

Let (μ∗∗,A∗∗)(\mu^{**}, \mathcal A^{**})(μ∗∗,A∗∗) be a local optimum of (15) with every area Ak∗∗>0A^{**}_k > 0Ak∗∗​>0. Then a decentralized equilibrium under μ∗∗\mu^{**}μ∗∗ exists, and every such equilibrium A∗\mathcal A^*A∗ satisfies

ν(Ak∗ △ Ak∗∗)=0(k=1,…,K).\nu\big(\mathcal A^*_k \,\triangle\, \mathcal A^{**}_k\big) = 0 \qquad (k = 1, \dots, K).ν(Ak∗​△Ak∗∗​)=0(k=1,…,K).

Since a global optimum is a local optimum, the best attainable social welfare is reached by decentralized customer behaviour once the rates are set correctly.

Milestones

  1. Lemma 6 (p. 34): with all areas prescribed, an optimal partition is {x∈S:gk−ηk≤gj−ηj}\{x \in \mathcal S : g_k - \eta_k \le g_j - \eta_j\}{x∈S:gk​−ηk​≤gj​−ηj​} for some constants η\etaη.
  2. Theorem 4 (p. 14): a local optimum Aˉ\bar{\mathcal A}Aˉ of (9)–(12) satisfies gk(x)+∂kG(Aˉ)≤gj(x)+∂jG(Aˉ)g_k(x) + \partial_k G(\bar A) \le g_j(x) + \partial_j G(\bar A)gk​(x)+∂k​G(Aˉ)≤gj​(x)+∂j​G(Aˉ) for almost every x∈Aˉkx \in \bar{\mathcal A}_kx∈Aˉk​.
  3. The rate step (p. 18): for fixed positive areas the rates μk=λAk+(B−λ)Ak/∑jAj\mu_k = \lambda A_k + (B - \lambda)\sqrt{A_k}/\sum_j \sqrt{A_j}μk​=λAk​+(B−λ)Ak​​/∑j​Aj​​ are the unique optimum, with value cB−λ(∑kAk)2\frac{c}{B-\lambda}\big(\sum_k \sqrt{A_k}\big)^2B−λc​(∑k​Ak​​)2.
  4. Theorem 5 (pp. 17–18): a local optimum of (15) is the weighted Voronoi diagram with weights wk∗∗=cB−λ(∑jAj∗∗)1Ak∗∗w^{**}_k = \frac{c}{B-\lambda}\big(\sum_j \sqrt{A^{**}_j}\big)\frac{1}{\sqrt{A^{**}_k}}wk∗∗​=B−λc​(∑j​Aj∗∗​​)Ak∗∗​​1​, and its rates are (17).
  5. The identity of p. 19: under (17), 1μk−λAk=1B−λ(∑jAj)1Ak\frac{1}{\mu_k - \lambda A_k} = \frac{1}{B-\lambda}\big(\sum_j \sqrt{A_j}\big)\frac{1}{\sqrt{A_k}}μk​−λAk​1​=B−λ1​(∑j​Aj​​)Ak​​1​.
  6. Lemma 1 part 2 (p. 8): the monotonicity of a cell's area in the relative weights.
  7. Cells partition S\mathcal SS (pp. 8, 10): ∑kAk(w)=1\sum_k A_k(w) = 1∑k​Ak​(w)=1 for every www.
  8. (1) ⇔ (3) (p. 8): equilibria are exactly the Voronoi diagrams solving (3).
  9. Theorem 2 (p. 10): the solution of (3) is unique.

Significance

The result separates two sources of inefficiency in spatial service systems. When the rates are fixed badly, decentralized choice can be far from socially optimal: in the paper's §4.2 example, customers crowd a high-capacity facility while a nearby one is underused. Theorem 6 shows that the inefficiency is not in the customers' behaviour but in the capacity allocation. With the rates of (17), a square-root rule in the areas, the planner gains nothing by dictating assignments. Theorem 4 is of independent interest for computational geometry. It identifies the Voronoi weights of a locally optimal partition with the gradient of the area cost, which makes nonconvex area costs tractable.

None of these results has a machine-checked proof. The mission produces formal statements of the paper's chain, from the semi-discrete inner problem (Lemma 6) through the first-order condition (Theorem 4) to the identification (Theorem 6), and invites proofs of each. The rate step is closely related to Kelly's square-root capacity allocation (Kelly 1979, Theorem 4.1), which is already proved on the platform and is included as a referenced item.

Difficulty

The weighted Voronoi part and the algebra of (16)–(17) are elementary. The weight lies in Theorem 4 and Lemma 6. The obvious argument moves a small amount of demand from one cell to another and compares costs to first order. It shows only that no pair of cells can be improved. It does not give a single vector of weights consistent across all KKK cells, and that is what Lemma 6 asserts: the existence of dual constants η\etaη for a semi-discrete transport problem. Existence of such constants requires the regularity condition and positive areas, and fails without them. Theorem 5 must also cope with a cost G(A)=cB−λ(∑kAk)2G(A) = \frac{c}{B-\lambda}(\sum_k \sqrt{A_k})^2G(A)=B−λc​(∑k​Ak​​)2 that is nonconvex and differentiable only at positive areas, and with a joint neighbourhood in rates and partitions.

Formalization scope

Lean works in EuclideanSpace ℝ (Fin n) with ν : Measure. Partitions are families Fin K → Set (E n) of measurable subsets of S\mathcal SS, covering S\mathcal SS, with ν\nuν-null pairwise intersections. Areas are (ν A).toReal. The waiting cost is real-valued: every equilibrium, feasibility condition and equilibrium-weight condition carries the stability conjunct λAk<μk\lambda A_k < \mu_kλAk​<μk​, replacing the paper's fk=∞f_k = \inftyfk​=∞. Under the goal's rates no equilibrium of the paper is lost by this. A local optimum means: for some ε>0\varepsilon > 0ε>0, no feasible competitor with ∣μk′−μk∣<ε|\mu'_k - \mu_k| < \varepsilon∣μk′​−μk​∣<ε and ν(Ak′△Ak)<ε\nu(\mathcal A'_k \triangle \mathcal A_k) < \varepsilonν(Ak′​△Ak​)<ε for all kkk is cheaper. Identities between partitions, and the pointwise conclusions of Lemma 6 and Theorems 4–5, hold up to ν\nuν-null sets. A null modification of an optimum is still optimal, so the pointwise readings are false.

Added hypotheses, each disclosed in the item: every dkd_kdk​ (resp. gkg_kgk​) is ν\nuν-integrable; the areas of the optimum in Theorems 5–6, and the prescribed areas in Lemma 6, are positive. The paper's positive intensity λ(x)>0\lambda(x) > 0λ(x)>0 is dropped, and Theorem 4 asks only for continuous gkg_kgk​ and GGG differentiable at Aˉ\bar AAˉ. Both changes weaken the hypotheses. The regularity condition is a push-forward measure absolutely continuous with respect to Lebesgue measure. The maps dj−dkd_j - d_kdj​−dk​ are continuous, so this condition is not vacuous.

A trivializing formalization is ruled out. The goal does not assume the optimum satisfies the Voronoi condition with weights (16), which would be Theorem 5. It quantifies over every decentralized equilibrium under μ∗∗\mu^{**}μ∗∗ and asserts that one exists.

Welcome contributions include semi-discrete optimal-transport duality (Lemma 6), nonatomic exchange arguments for partitions, and measure-level facts about weighted Voronoi diagrams. All of these are reusable well beyond this paper.

Selected references

  • J. G. Carlsson, X. Peng, I. O. Ryzhov, Demand Equilibria in Spatial Service Systems, SSRN preprint 4794156 (version of 2024-04-14); published in Manufacturing & Service Operations Management, 2024. https://ssrn.com/abstract=4794156, https://doi.org/10.1287/msom.2023.0434
  • F. P. Kelly, Reversibility and Stochastic Networks, Wiley, 1979, Theorem 4.1 (square-root capacity allocation); proved on Prove2Me as KellyReversibility.Allocation.optimal_capacity_allocation.
  • J. G. Carlsson, E. Carlsson, R. Devulapalli, Shadow Prices in Territory Division, Networks and Spatial Economics 16 (2016), 893–931.
  • V. Hartmann, D. Schuhmacher, Semi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case, Mathematical Methods of Operations Research 92 (2020), 133–163.
12 thms3 active usersReviewed
Dynamic ProgrammingOperations ResearchOptimization+1·Captain: Shuze Chen

Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models: The Sampled Upper Base-Stock Policy Costs at Most (1 + ε) Times the Optimum with Probability 1 − δResearch Paper

Motivation

The periodic-review inventory problem asks how much stock to order in each of TTT periods when demand is random, unmet demand is backlogged, and both leftover stock and shortages are charged. When the demand distributions are known, a backward dynamic program computes an optimal policy, and the optimal policy is a base-stock policy: in each period, order up to a fixed target level (Zipkin, Foundations of Inventory Management, 2000). In practice the distributions are rarely known; what is available is data, or a simulator that produces demand scenarios.

Levi, Roundy and Shmoys (Math. Oper. Res. 32(4), 2007) asked how many sampled demand scenarios suffice to compute a policy whose expected cost is provably within a factor 1+ε1+\varepsilon1+ε of optimal, with a sample size that depends on ε\varepsilonε, the confidence δ\deltaδ, the horizon and the cost parameters, but not on the demand distributions. For the single-period newsvendor problem they proved such a bound for sample average approximation (their Theorem 2.2). For the multiperiod model they designed a new algorithm and proved the bound that this mission targets (Theorem 3.5 of §3.3).

Setting

Periods are indexed t=0,…,T−1t = 0, \dots, T-1t=0,…,T−1 (the paper counts from 111). The demand DtD_tDt​ of period ttt is a real random variable with law μt\mu_tμt​ and finite mean, possibly negative. Demands of different periods are independent but need not be identically distributed. Orders arrive immediately, there is no ordering cost, and unmet demand is backlogged. Raising the inventory to yyy in period ttt costs, at the end of the period, h(y−Dt)++b(Dt−y)+h(y - D_t)^+ + b(D_t - y)^+h(y−Dt​)++b(Dt​−y)+ with h,b>0h, b > 0h,b>0.

  • The newsvendor cost is Ct(y)=E[h(y−Dt)++b(Dt−y)+]C_t(y) = \mathbb E[h(y-D_t)^+ + b(D_t-y)^+]Ct​(y)=E[h(y−Dt​)++b(Dt​−y)+].
  • The optimal dynamic program (eqs. (1)–(2)) is Ut(y)=Ct(y)+E[Vt+1(y−Dt)]U_t(y) = C_t(y) + \mathbb E[V_{t+1}(y - D_t)]Ut​(y)=Ct​(y)+E[Vt+1​(y−Dt​)] and Vt(x)=min⁡y≥xUt(y)V_t(x) = \min_{y \ge x} U_t(y)Vt​(x)=miny≥x​Ut​(y), with VT≡0V_T \equiv 0VT​≡0. The optimal expected cost is OPT=min⁡yU0(y)\mathrm{OPT} = \min_y U_0(y)OPT=miny​U0​(y).
  • For levels L=(Lt)L = (L_t)L=(Lt​), the base-stock policy raises the inventory xxx to max⁡(x,Lt)\max(x, L_t)max(x,Lt​) in period ttt. The shadow cost U~t(y)\tilde U_t(y)U~t​(y) is the expected cost over periods t,…,T−1t, \dots, T-1t,…,T−1 of ordering up to yyy in period ttt and then following Lt+1,…,LT−1L_{t+1}, \dots, L_{T-1}Lt+1​,…,LT−1​.
  • Ls,…,LT−1L_s, \dots, L_{T-1}Ls​,…,LT−1​ is an upper base-stock policy (Definition 3.2) if for each j≥sj \ge sj≥s the shadow cost U~j\tilde U_jU~j​ is convex with smallest minimizer Rˉj\bar R_jRˉj​, and Lj≥RˉjL_j \ge \bar R_jLj​≥Rˉj​.
  • A point yyy is an α\alphaα-point of fff (Definition 3.3) if fff has a subgradient rrr at yyy with ∣r∣≤α|r| \le \alpha∣r∣≤α.
  • The sample right-derivative estimator U^tr(y)\hat U^r_t(y)U^tr​(y) averages, over NNN sampled demand paths, an explicit bounded path variable whose mean is the right derivative of U~t\tilde U_tU~t​ (Lemma 3.4, Corollary 3.1).

The algorithm (§3.3, p. 835). Given 0<ε≤10 < \varepsilon \le 10<ε≤1 and 0<δ<10 < \delta < 10<δ<1, put εt=ε/2T\varepsilon_t = \varepsilon/2Tεt​=ε/2T, δt=δ/T\delta_t = \delta/Tδt​=δ/T and αt=(εt/3)min⁡(b,h)\alpha_t = (\varepsilon_t/3)\min(b, h)αt​=(εt​/3)min(b,h). The paper states:

In stage t=T,…,1t = T, \dots, 1t=T,…,1, consider the function U~t\tilde U_tU~t​ defined above with respect to the previously computed base-stock levels R~t+1,…,R~T\tilde R_{t+1}, \dots, \tilde R_TR~t+1​,…,R~T​ (where U~T=CT\tilde U_T = C_TU~T​=CT​). Use the black box to generate Nt=2((b+h)(T−t+1))2(1/αt2)log⁡(2/δt)N_t = 2((b + h)(T - t + 1))^2(1/\alpha_t^2)\log(2/\delta_t)Nt​=2((b+h)(T−t+1))2(1/αt2​)log(2/δt​) independent samples of the demands Dt,…,DTD_t, \dots, D_TDt​,…,DT​ and compute R~t=inf⁡{y:U^tr(y)≥αt/2}\tilde R_t = \inf\{y : \hat U^r_t(y) \ge \alpha_t/2\}R~t​=inf{y:U^tr​(y)≥αt​/2}. Note that in each stage, the algorithm is using an additional NtN_tNt​ samples that are independent of the samples used in previous stages.

Formalization targets

Goal: Theorem 3.5 (§3.3)

For T≥1T \ge 1T≥1, h,b>0h, b > 0h,b>0, independent demand laws with finite mean, 0<ε≤10 < \varepsilon \le 10<ε≤1 and 0<δ<10 < \delta < 10<δ<1, the levels R~0,…,R~T−1\tilde R_0, \dots, \tilde R_{T-1}R~0​,…,R~T−1​ computed by the algorithm satisfy

Pr⁡[ U~0(R~0)≤(1+ε) min⁡yU0(y) ]  ≥  1−δ.\Pr\Big[\ \tilde U_0(\tilde R_0) \le (1+\varepsilon)\,\min_y U_0(y)\ \Big] \;\ge\; 1-\delta .Pr[ U~0​(R~0​)≤(1+ε)ymin​U0​(y) ]≥1−δ.

Here U~0(R~0)\tilde U_0(\tilde R_0)U~0​(R~0​) is the expected cost of the computed policy and min⁡yU0(y)\min_y U_0(y)miny​U0​(y) is the optimal expected cost. The constants are those of the paper; the goal fixes the algorithm, not a class of algorithms.

Milestones (proof order)

  1. Fact 3.1: w(x)=min⁡y≥xf(y)w(x) = \min_{y\ge x} f(y)w(x)=miny≥x​f(y) is convex for convex fff with a minimizer rrr, and w(x)=f(max⁡(x,r))w(x) = f(\max(x, r))w(x)=f(max(x,r)).
  2. Lemma 3.1: following an upper base-stock policy keeps U~j\tilde U_jU~j​ and V~j\tilde V_jV~j​ convex, so Rˉj\bar R_jRˉj​ exists.
  3. Lemma 3.2: if each tail policy is βj\beta_jβj​-optimal, ordering up to Rˉt\bar R_tRˉt​ is max⁡jβj\max_j \beta_jmaxj​βj​-optimal.
  4. Claim after Lemma 3.2 (p. 832): per-stage factors (1+εt)(1+\varepsilon_t)(1+εt​) multiply to ∏j(1+εj)\prod_j(1+\varepsilon_j)∏j​(1+εj​).
  5. Lemma 3.3: an α\alphaα-point of a convex f≥h(y−d)++b(d−y)+f \ge h(y-d)^+ + b(d-y)^+f≥h(y−d)++b(d−y)+, with α=(ε/3)min⁡(b,h)\alpha = (\varepsilon/3)\min(b,h)α=(ε/3)min(b,h), is (1+ε)(1+\varepsilon)(1+ε)-optimal.
  6. Lemma 3.4: the path variable M~tjr(y)\tilde M^r_{tj}(y)M~tjr​(y) has the mean of 1(A~jt(y))Cjr(y−D[t,j))\mathbb 1(\tilde A_{jt}(y))C^r_j(y - D_{[t,j)})1(A~jt​(y))Cjr​(y−D[t,j)​).
  7. Corollary 3.1: the right derivative of U~t\tilde U_tU~t​ is the mean of the path value.
  8. Lemma 3.5: one stage with NtN_tNt​ samples produces an αt\alpha_tαt​-point R~t≥Rˉt\tilde R_t \ge \bar R_tR~t​≥Rˉt​ with probability at least 1−δt1 - \delta_t1−δt​.

Hoeffding's inequality (Theorem 3.4) enters through Lemma 3.5; Concentration.hoeffding_two_sided is included as a reference item.

Significance

The theorem gives sample sizes NtN_tNt​ that grow polynomially in TTT, 1/ε1/\varepsilon1/ε, log⁡(1/δ)\log(1/\delta)log(1/δ) and (b+h)/min⁡(b,h)(b+h)/\min(b,h)(b+h)/min(b,h), and are uniform over all demand distributions with finite mean. The analysis shows that a sampled dynamic program need not lose control of convexity: the upper base-stock invariant keeps every stage a convex problem, and relative errors made in later stages multiply rather than compound through the recursion.

The result has been proved on paper since 2007. As far as a search of the platform shows (October 2026), none of its lemmas has a machine-checked proof; the platform has single-period newsvendor material from other papers, and a two-sided Hoeffding inequality. A formalization would supply a finite-horizon stochastic inventory dynamic program and its shadow version as reusable objects, a one-dimensional bridge from approximate first-order optimality to relative error (Lemma 3.3), and an end-to-end checked sample-complexity bound for a multistage stochastic model.

Difficulty

The naive argument estimates each stage's minimizer by sampling and adds up the per-stage errors. It fails twice. First, once a later stage uses an approximate level, the earlier stage's cost-to-go is no longer that of an optimal policy and need not be convex, so "the minimizer" may not exist and its sampled estimate has no guarantee. Second, an additive error in a later stage's cost can be amplified by the earlier stages. The paper's invariant, which requires each computed level to lie above the true shadow minimizer, controls both, and the formalization has to establish this invariant from samples with high probability. Measure-theoretically, the costs are nested expectations of infima, a one-sided derivative must pass through an expectation over a demand path, and stage-wise events must be combined although earlier levels are random.

Formalization scope

  • Conventions. Periods are 000-based natural numbers, so the paper's factor T−t+1T - t + 1T−t+1 is T−tT - tT−t, and the paper's R~1\tilde R_1R~1​ is R~0\tilde R_0R~0​. Laws μt\mu_tμt​ and costs are given for every t∈Nt \in \mathbb Nt∈N; only t<Tt < Tt<T matter. Demands are real valued. Expectations are Bochner integrals, and every theorem that evaluates a cost function assumes E∣Dt∣<∞\mathbb E|D_t| < \inftyE∣Dt​∣<∞, the paper's standing assumption. min⁡y≥x\min_{y \ge x}miny≥x​ and min⁡y\min_yminy​ are real infima of nonnegative functions. "Rˉj\bar R_jRˉj​ exists" means "U~j\tilde U_jU~j​ is convex and has a smallest minimizer". The paper's real NtN_tNt​ is rounded up with ⌈⋅⌉\lceil\cdot\rceil⌈⋅⌉; Lemma 3.5 only needs NtN_tNt​ to be at least the bound. The right derivative is HasDerivWithinAt on [y,∞)[y, \infty)[y,∞). Probabilities of events are measures of sets, which are outer measures on non-measurable sets.
  • Sample space. For each stage t<Tt < Tt<T, NtN_tNt​ demand paths in RT\mathbb R^TRT; all paths are independent with law μ0⊗⋯⊗μT−1\mu_0 \otimes \dots \otimes \mu_{T-1}μ0​⊗⋯⊗μT−1​. A stage-ttt path also has coordinates for periods before ttt, which are never read. R~t\tilde R_tR~t​ is a deterministic function of the outcome, defined by backward recursion from the paper's infimum rule.
  • No trivialization. The optimum is defined from the true dynamic program (1)–(2) and does not depend on the algorithm. The algorithm is fixed, with the paper's NtN_tNt​, αt\alpha_tαt​ and δt\delta_tδt​. Samples are independent across stages. The demand laws are arbitrary probability measures on R\mathbb RR with finite mean, not restricted to bounded, continuous or nonnegative demand.
  • Infrastructure needed. Convexity of parametric infima, interchange of one-sided derivatives with expectations, conditioning on products of independent coordinates, and Hoeffding's inequality (one-sided, in Mathlib in sub-Gaussian form). The model, shadow program and estimator definitions are reusable for other inventory and sampled dynamic-programming results. Proofs of any milestone, including partial results such as the single-stage case T=1T = 1T=1, are welcome.

Selected references

  • R. Levi, R. O. Roundy, D. B. Shmoys, Provably Near-Optimal Sampling-Based Policies for Stochastic Inventory Control Models, Mathematics of Operations Research 32(4):821–839, 2007. https://doi.org/10.1287/moor.1070.0272
  • P. H. Zipkin, Foundations of Inventory Management, McGraw-Hill, 2000.
  • W. Hoeffding, Probability Inequalities for Sums of Bounded Random Variables, Journal of the American Statistical Association 58(301):13–30, 1963. https://doi.org/10.1080/01621459.1963.10500830
15 thms3 active usersReviewed
Dynamic ProgrammingOperations ResearchProbability+1·Captain: Shuze Chen

Average Cost Markov Decision Processes with Weakly Continuous Transition Probabilities: Assumptions (W*) and (B̲) Give the Average Cost Optimality Inequality and a Stationary Optimal PolicyResearch Paper

Motivation

A Markov decision process (MDP) with the average cost per unit time criterion models a controller who runs a stochastic system indefinitely and cares about long-run cost rather than any finite horizon: inventory control, queueing control, and maintenance are standard examples. The basic questions are whether an optimal policy exists, whether it can be taken stationary (a fixed measurable rule ϕ(x)\phi(x)ϕ(x)), and whether it is characterized by an average cost optimality inequality (ACOI).

For uncountable state spaces the classical answer is Schäl's (Math. Oper. Res. 18, 1993), under weak continuity of the transition probabilities, compact action sets, and a uniform bound on the relative discounted values. Hernández-Lerma replaced compactness by inf-compactness of the cost in the action, but under setwise continuity; Feinberg and Lewis (Math. Oper. Res. 32, 2007) allowed noncompact actions under weak continuity, at the price of inf-compactness of the cost in state and action jointly plus a local boundedness condition. That joint inf-compactness excludes bounded costs on unbounded state spaces, the setting of Ross and of Dynkin and Yushkevich. Feinberg, Kasyanov and Zadoianchuk (Math. Oper. Res. 37(4), 2012) remove both restrictions.

Setting

The state space X\mathbb XX and action space A\mathbb AA are Borel subsets of Polish spaces. Each state xxx has a nonempty Borel set A(x)⊆AA(x)\subseteq\mathbb AA(x)⊆A of available actions; the graph Gr(A)={(x,a):a∈A(x)}\mathrm{Gr}(A)=\{(x,a):a\in A(x)\}Gr(A)={(x,a):a∈A(x)} is Borel and has a measurable selector. Choosing aaa at xxx costs c(x,a)∈(−∞,+∞]c(x,a)\in(-\infty,+\infty]c(x,a)∈(−∞,+∞] (bounded below, measurable) and moves the system according to a transition probability q(⋅∣x,a)q(\cdot\mid x,a)q(⋅∣x,a). A policy π\piπ is a sequence of randomized, history-dependent decision rules concentrated on A(xn)A(x_n)A(xn​); the set of stationary policies F\mathbb FF consists of Borel maps ϕ\phiϕ with ϕ(x)∈A(x)\phi(x)\in A(x)ϕ(x)∈A(x).

For α∈[0,1)\alpha\in[0,1)α∈[0,1) the discounted cost is vαπ(x)=Exπ∑n≥0αnc(xn,an)v^\pi_\alpha(x)=\mathbb E^\pi_x\sum_{n\ge0}\alpha^n c(x_n,a_n)vαπ​(x)=Exπ​∑n≥0​αnc(xn​,an​) and the average cost is wπ(x)=lim sup⁡N→∞1NExπ∑n<Nc(xn,an)w^\pi(x)=\limsup_{N\to\infty}\frac1N\mathbb E^\pi_x\sum_{n<N}c(x_n,a_n)wπ(x)=limsupN→∞​N1​Exπ​∑n<N​c(xn​,an​). Optimal values are infima over all policies: vα(x)v_\alpha(x)vα​(x), w(x)w(x)w(x), and w∗=inf⁡xw(x)w^*=\inf_x w(x)w∗=infx​w(x). Put mα=inf⁡xvα(x)m_\alpha=\inf_x v_\alpha(x)mα​=infx​vα​(x), the relative discounted value uα=vα−mαu_\alpha=v_\alpha-m_\alphauα​=vα​−mα​, and wˉ=lim sup⁡α↑1(1−α)mα\bar w=\limsup_{\alpha\uparrow1}(1-\alpha)m_\alphawˉ=limsupα↑1​(1−α)mα​.

The assumptions are:

  • (W*): (i) ccc is lower semicontinuous on Gr(A)\mathrm{Gr}(A)Gr(A); (ii) if xn→xx_n\to xxn​→x in X\mathbb XX, an∈A(xn)a_n\in A(x_n)an​∈A(xn​) and c(xn,an)c(x_n,a_n)c(xn​,an​) is bounded above, then (an)(a_n)(an​) has a limit point in A(x)A(x)A(x); (iii) q(⋅∣x,a)q(\cdot\mid x,a)q(⋅∣x,a) is weakly continuous in (x,a)∈Gr(A)(x,a)\in\mathrm{Gr}(A)(x,a)∈Gr(A).
  • (B̲): w∗<∞w^*<\inftyw∗<∞, and lim inf⁡α↑1uα(x)<∞\liminf_{\alpha\uparrow1}u_\alpha(x)<\inftyliminfα↑1​uα​(x)<∞ for every xxx.
  • (Wu): ccc is inf-compact on Gr(A)\mathrm{Gr}(A)Gr(A), and (W*)(iii) holds.

The candidate relative value function is the joint lower limit

u(x)=lim inf⁡α↑1,  y→xuα(y),u(x)=\liminf_{\alpha\uparrow1,\;y\to x}u_\alpha(y),u(x)=α↑1,y→xliminf​uα​(y),

and the candidate optimal actions are A∗(x)={a∈A(x): wˉ+u(x)≥c(x,a)+∫Xu(y) q(dy∣x,a)}A^*(x)=\{a\in A(x):\ \bar w+u(x)\ge c(x,a)+\int_{\mathbb X}u(y)\,q(dy\mid x,a)\}A∗(x)={a∈A(x): wˉ+u(x)≥c(x,a)+∫X​u(y)q(dy∣x,a)}.

Formalization targets

Goal: Theorem 3

Under (W*) and (B̲) there is ϕ∈F\phi\in\mathbb Fϕ∈F with

wˉ+u(x) ≥ c(x,ϕ(x))+∫Xu(y) q(dy∣x,ϕ(x)),x∈X,\bar w+u(x)\ \ge\ c(x,\phi(x))+\int_{\mathbb X}u(y)\,q(dy\mid x,\phi(x)),\qquad x\in\mathbb X,wˉ+u(x) ≥ c(x,ϕ(x))+∫X​u(y)q(dy∣x,ϕ(x)),x∈X,

and for it w(x)=wϕ(x)=lim sup⁡α↑1(1−α)vα(x)=wˉ=w∗w(x)=w^\phi(x)=\limsup_{\alpha\uparrow1}(1-\alpha)v_\alpha(x)=\bar w=w^*w(x)=wϕ(x)=limsupα↑1​(1−α)vα​(x)=wˉ=w∗ for all xxx. Moreover (a) uuu is finite and lower semicontinuous; (b) each A∗(x)A^*(x)A∗(x) is nonempty and compact and Gr(A∗)\mathrm{Gr}(A^*)Gr(A∗) is Borel; (c) every ϕ∈F\phi\in\mathbb Fϕ∈F selecting from A∗A^*A∗ is average cost optimal and satisfies the inequality; (d) some ϕ∈F\phi\in\mathbb Fϕ∈F selects from the minimizer sets A∗(x)⊆A∗(x)A_*(x)\subseteq A^*(x)A∗​(x)⊆A∗(x) of a↦c(x,a)+∫u dqa\mapsto c(x,a)+\int u\,dqa↦c(x,a)+∫udq; (e) under (Wu), uuu is inf-compact.

Milestones

In the paper's order: Theorem 1 (the inequality with wˉ\bar wwˉ implies optimality), Lemma 2 (inf-compactness in the action), Lemma 3 (lower semicontinuity of the minimal value and a Borel minimizer), Lemma 4 (a Fatou lemma for weakly converging measures, already on the platform as Schäl's Lemma 2.3(ii)), Theorem 2 (i), (iv)–(vi) (the discounted problem under (W*)), Lemma 6 (semicontinuity of uuu, uαu_\alphauα​ and the auxiliary u‾α\underline u_\alphau​α​), Corollary 1, and Lemma 7, the ACOI itself. Schäl's inequality w‾≤wˉ≤w∗<∞\underline w\le\bar w\le w^*<\inftyw​≤wˉ≤w∗<∞ is referenced as an existing platform item.

Significance

Theorem 3 gives stationary average-cost optimal policies for weakly continuous MDPs with noncompact action sets and costs that need not be inf-compact in the state, which covers bounded costs on unbounded state spaces and inventory models whose holding costs do not grow without bound. Assumption (B̲) asks only for a finite lower limit of uα(x)u_\alpha(x)uα​(x) as α↑1\alpha\uparrow1α↑1, weaker than Schäl's uniform bound. The approach was extended to partially observable MDPs and to later work on the average cost optimality equation, which builds on exactly these objects.

The theorem is proved in the literature; nothing here is open mathematically. What the mission adds is a machine-checked proof of the full chain from measurable selection and semicontinuity through the vanishing-discount limit, for Borel spaces and history-dependent randomized policies. Reading the proofs closely has turned up three printed statements that are false as written (see Beyond the paper); a formal development settles exactly which hypotheses each step needs.

Difficulty

The obvious route takes a limit of discount-optimal policies ϕαn\phi_{\alpha_n}ϕαn​​ as αn↑1\alpha_n\uparrow1αn​↑1. With noncompact A(x)A(x)A(x) such a sequence need not have any limit point; (W*)(ii) supplies one only when the costs along it stay bounded, and that has to be extracted from the relative values. The second obstacle is that uαu_\alphauα​ is controlled only through its lower limit at each point, so uuu is a joint lower limit in (α,y)(\alpha,y)(α,y) and passing it under ∫⋅ dq\int\cdot\,dq∫⋅dq requires a Fatou-type lemma for weakly converging measures rather than dominated convergence. Finally, selecting a Borel ϕ\phiϕ from the sets A∗(x)A^*(x)A∗(x) requires a uniformization theorem for Borel sets with σ\sigmaσ-compact sections (Arsenin–Kunugui), which Mathlib does not have.

Formalization scope

The spaces are types with a separable metrizable topology whose Borel σ\sigmaσ-algebra is standard Borel (by Lusin–Souslin, exactly the Borel subsets of Polish spaces up to homeomorphism). The model reuses the published definitions FeinbergLiang_ACOE_MDP (policies, path measures via Ionescu–Tulcea, discounted and average costs) and SchalAvg_AvgOpt_Model (action sets, admissible policies, vαv_\alphavα​, mαm_\alphamα​, uαu_\alphauα​, wˉ\bar wwˉ, w∗w^*w∗, Assumption (G)). Conventions:

  • the cost takes values in [0,+∞][0,+\infty][0,+∞]; the paper's bounded-below cost reduces to this by adding a constant, which changes none of the statements (p. 595);
  • qqq is a Markov kernel on all of X×A\mathbb X\times\mathbb AX×A; only its values on Gr(A)\mathrm{Gr}(A)Gr(A) enter;
  • functions such as uuu are [0,+∞][0,+\infty][0,+∞]-valued, and the paper's "u:X→R+u:\mathbb X\to\mathbb R_+u:X→R+​" is stated as u(x)≠+∞u(x)\neq+\inftyu(x)=+∞ for every xxx;
  • α↑1\alpha\uparrow1α↑1 is the left neighbourhood filter of 111; uuu is the lower limit along N<(1)×N(x)\mathcal N_{<}(1)\times\mathcal N(x)N<​(1)×N(x), with y=xy=xy=x allowed;
  • "min" is stated as an attained infimum;
  • the existence of a measurable selector of AAA is the hypothesis HasSelector.

Optimal values are infima over all admissible (randomized, history-dependent) policies, not over stationary ones. uuu is the fixed function (21), never a free parameter. All four equalities of (10) are stated for every xxx. (W*), (Wu) and (B̲) constrain ccc and qqq on Gr(A)\mathrm{Gr}(A)Gr(A) only. A statement that ranged over a smaller policy class, left uuu free, kept a single equality, or imposed continuity on all of X×A\mathbb X\times\mathbb AX×A would be a different and easier theorem.

A complete development needs: semicontinuity and inf-compactness calculus for [0,∞][0,\infty][0,∞]-valued functions; lower semicontinuity of μ↦∫f dμ\mu\mapsto\int f\,d\muμ↦∫fdμ under weak convergence for lower semicontinuous fff; the Arsenin–Kunugui uniformization theorem; discounted dynamic programming on Borel spaces; and an Abelian/Tauberian comparison of lim sup⁡(1−α)vα\limsup(1-\alpha)v_\alphalimsup(1−α)vα​ with Cesàro averages. The first three are reusable well beyond MDPs. Contributions to any milestone, or to these libraries, are welcome.

Beyond the paper. Three corrected variants are posed beside the faithful statements, which stay as printed. Lemma 2, corrected: as printed it assumes only (W*)(ii) (and (iii)), but without lower semicontinuity of ccc a level set such as (0,1](0,1](0,1] need not be compact; the variant adds (W*)(i). Lemma 3, corrected: u∗∈L(X)u^*\in L(\mathbb X)u∗∈L(X) can fail because u∗u^*u∗ may be identically +∞+\infty+∞ for some u∈L(X)u\in L(\mathbb X)u∈L(X); the variant asserts lower semicontinuity only. Corollary 1, corrected: the joint limit lim⁡n→∞, y→xu‾αn(y)\lim_{n\to\infty,\,y\to x}\underline u_{\alpha_n}(y)limn→∞,y→x​u​αn​​(y) need not exist (a one-action example with an indicator cost); the variant uses the lower limit, which is what the proof computes and what Lemma 7 uses. A misprint in the proof of Lemma 3 (p. 598, Gr(A)\mathrm{Gr}(A)Gr(A) for Gr(A∗)\mathrm{Gr}(A_*)Gr(A∗​)) does not affect its statement.

Selected references

  • E. A. Feinberg, P. O. Kasyanov, N. V. Zadoianchuk, Average cost Markov decision processes with weakly continuous transition probabilities, Mathematics of Operations Research 37(4):591–607, 2012. https://doi.org/10.1287/moor.1120.0555
  • M. Schäl, Average optimality in dynamic programming with general state space, Mathematics of Operations Research 18(1):163–172, 1993. https://doi.org/10.1287/moor.18.1.163
  • E. A. Feinberg, M. E. Lewis, Optimality inequalities for average cost Markov decision processes and the stochastic cash balance problem, Mathematics of Operations Research 32(4):769–783, 2007. https://doi.org/10.1287/moor.1070.0269
  • O. Hernández-Lerma, J. B. Lasserre, Discrete-Time Markov Control Processes: Basic Optimality Criteria, Springer, 1996. https://doi.org/10.1007/978-1-4612-0729-0
  • A. S. Kechris, Classical Descriptive Set Theory, Springer, 1995 (Arsenin–Kunugui theorem, p. 297). https://doi.org/10.1007/978-1-4612-4190-4
13 thms3 active usersReviewed
Quantum Information·Captain: Goku

Entanglement-Assisted Capacity of Quantum Multiple-Access ChannelsResearch Paper

Motivation

We find a regularized formula for the entanglement-assisted (EA) capacity region for quantum multiple access channels (QMAC). We illustrate the capacity region calculation with the example of the collective phase-flip channel which admits a single-letter characterization. On the way, we provide a first-principles proof of the EA coding theorem based on a packing argument. We observe that the Holevo-Schumacher-Westmoreland theorem may be obtained from a modification of our EA protocol. We remark on the existence of a family hierarchy of protocols for multiparty scenarios with a single receiver, in analogy to the two-party case. In this way, we relate several previous results regarding QMACs.

This mission is the statement-level formalization of Entanglement-Assisted Capacity of Quantum Multiple-Access Channels (arXiv:quant-ph/0511228v2). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

10 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is Entanglement-assisted QMAC capacity region (Theorem 6, (42)–(44), PDF p. 6). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Deferred results

6 results of the paper are not stated yet, because the library lacks an interface they need:

  • §V: finite-entanglement classical corner resource inequalities (67)–(68): Finite-ebit QMAC protocol with explicit dilution transcript and vanishing-rate directed classical communication (hard)
  • §V: QMAC father resource inequalities (69)–(70): Three-party QP protocol/resource semantics and constructive Rule O/catalytic cancellation bridge (hard)
  • Single-sender specialization of the coherification claim and (69): Resource-counted QP adapter from this particular finite EAC family to the single-sender father conversion (hard)
  • §V: unassisted quantum conversions (71)–(72), source correction: Signed quantum resource rates and a finite conversion implementing ebit replacement/cancellation (hard)
  • §V: one-sender coherification and cq entanglement removal: Hybrid classical/quantum signed resource protocol and Schmidt-aligned cq entanglement removal (hard)
  • §VI conjecture, continuing p.11: An established s-sender EA coding theorem and its precise extended operational model (source-ambiguity)

Selected references

  • Entanglement-Assisted Capacity of Quantum Multiple-Access Channels, arXiv:quant-ph/0511228v2, https://arxiv.org/abs/quant-ph/0511228v2
18 thms3 active usersReviewed
Quantum Information·Captain: Goku

On entanglement-assisted classical capacityResearch Paper

Motivation

This paper is essentially a lecture from the author's course on quantum information theory, which is devoted to the result of C. H. Bennett, P. W. Shor, J. A. Smolin and A. V. Thapliyal (quant-ph/0106052) concerning entanglement-assisted classical capacity of a quantum channel. A modified proof of this result is given and relation between entanglement-assisted and unassisted classical capacities is discussed.

This mission is the statement-level formalization of On entanglement-assisted classical capacity (arXiv:quant-ph/0106075v2). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

15 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is BSST capacity as an attained mutual-information maximum (§II, equation (4): BSST entanglement-assisted classical capacity, PDF p. 2). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Deferred results

3 results of the paper are not stated yet, because the library lacks an interface they need:

  • PDF p.4, (10): printed arbitrary-function empirical estimate (source-ambiguity)
  • PDF p.4, (11): printed strong-to-entropy strict tolerance (source-ambiguity)
  • PDF p.8, final paragraph: meaning and intercept of the claimed asymptotic line (source-ambiguity)

Selected references

  • On entanglement-assisted classical capacity, arXiv:quant-ph/0106075v2, https://arxiv.org/abs/quant-ph/0106075v2
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Quantum Information·Captain: Goku

Public and private resource trade-offs for a quantum channelResearch Paper

Motivation

Collins and Popescu realized a powerful analogy between several resources in classical and quantum information theory. The Collins-Popescu analogy states that public classical communication, private classical communication, and secret key interact with one another somewhat similarly to the way that classical communication, quantum communication, and entanglement interact. This paper discusses the information-theoretic treatment of this analogy for the case of noisy quantum channels. We determine a capacity region for a quantum channel interacting with the noiseless resources of public classical communication, private classical communication, and secret key. We then compare this region with the classical-quantum-entanglement region from our prior efforts and explicitly observe the information-theoretic consequences of the strong correlations in entanglement and the lack of a super-dense coding protocol in the public-private-secret-key setting. The region simplifies for several realistic, physically-motivated channels such as entanglement-breaking channels, Hadamard channels, and quantum erasure channels, and we are able to compute and plot the region for several examples of these channels.

This mission is the statement-level formalization of Public and private resource trade-offs for a quantum channel (arXiv:1005.3818v3). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

24 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is Private dynamic capacity: operational region equals the regularized entropic region (Theorem 1, PDF p. 5). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Deferred results

1 results of the paper are not stated yet, because the library lacks an interface they need:

  • Lemma 1: The scope of additivity needed to infer regularized single-letterization from a channel-pair equality. (source-ambiguity)

Selected references

  • Public and private resource trade-offs for a quantum channel, arXiv:1005.3818v3, https://arxiv.org/abs/1005.3818v3
33 thms3 active usersReviewed
Quantum Information·Captain: Goku

The Capacity of the Quantum Multiple Access ChannelResearch Paper

Motivation

We define classical-quantum multiway channels for transmission of classical information, after recent work by Allahverdyan and Saakian. Bounds on the capacity region are derived in a uniform way, which are analogous to the classically known ones, simply replacing Shannon entropy with von Neumann entropy. For the single receiver case (multiple access channel) the exact capacity region is determined. These results are applied to the case of noisy channels, with arbitrary input signal states. A second issue of this work is the presentation of a calculus of quantum information quantities, based on the algebraic formulation of quantum theory.

This mission is the statement-level formalization of The Capacity of the Quantum Multiple Access Channel (arXiv:quant-ph/9807019v3). Every numbered result of the paper was scanned. The results stated here are open sorry goals, ready for solvers to prove.

What is formalized

10 statements of the paper are in Lean 4 (Mathlib 0df444a). The goal theorem is Exact capacity region of a finite-dimensional quantum multiple-access channel (Theorem 10, PDF p. 6). The milestones are the other numbered results, in the paper's order, each quoting the paper.

The paper's own objects are the definition item of this mission. Objects shared across papers come from the prove2quantum library and from earlier papers' missions, listed as references.

Provenance: prove2quantum's pipeline drafted each statement (gpt-6.1-sol on Stanford's Sherlock cluster). A blind read-back, written without seeing the paper, is attached to every item. An independent moderator compared each statement against the paper and passed the whole paper without edits.

Deferred results

2 results of the paper are not stated yet, because the library lacks an interface they need:

  • §VII, product-input converse: Operational product-input quantum multiway codes, their intrinsic-algebra block-channel action and all-large-length average-error capacity region (hard)
  • §VII, 1-separable capacity region: Independent operational product-input and per-sender fully temporally separable-input MAC regions, with finite-ensemble information union (hard)

Selected references

  • The Capacity of the Quantum Multiple Access Channel, arXiv:quant-ph/9807019v3, https://arxiv.org/abs/quant-ph/9807019v3
16 thms3 active usersReviewed
Algorithmic Game TheoryEconomics·Captain: mikedeng1

Interactive Information Design: Every Information Design Game with Finite Message Sets Admits a Subgame Perfect EquilibriumResearch Paper

Why this game matters

An information designer can change what others know without choosing their actions. When several designers disclose information at once, each designer's experiment affects the incentives faced by agents who receive messages from all of them. The resulting strategic problem appears in competing disclosures, advice, and mediated decisions. Koessler, Laclau, and Tomala study this interaction in a finite-state model and ask whether the designers and agents can play consistently at every continuation of the game. Their working paper proves an existence result for this general model before examining special splitting, corporation, and multi-period games.

The existence question has a specific obstacle. After seeing the designers' experiments, agents may have several optimal continuation behaviors. A designer's payoff can jump as those behaviors change with the experiment profile. The paper's Example 5 shows that some ways of selecting an agent equilibrium at each profile yield an induced game between designers with no approximate equilibrium. The continuation behavior must therefore be part of what the equilibrium chooses; a fixed arbitrary rule cannot be presumed adequate. The paper invokes the equilibrium-selection result of Simon and Zame (1990) for this step. The example and the proof are in the working paper, pp. 6–7 and 21–23.

Finite information design games

Let NNN be a nonempty finite set of designers and KKK a nonempty finite set of agents. Designer iii holds a component θi\theta_iθi​ of the state, drawn with a common prior p0p^0p0 on Θ=∏i∈NΘi\Theta=\prod_{i\in N}\Theta_iΘ=∏i∈N​Θi​. The prior is general: its coordinates may be dependent. Agent jjj chooses an action in a nonempty finite set AjA_jAj​, and the action profile lies in A=∏j∈KAjA=\prod_{j\in K}A_jA=∏j∈K​Aj​. Designer iii and agent jjj receive real payoffs ui(a;θ)u_i(a;\theta)ui​(a;θ) and vj(a;θ)v_j(a;\theta)vj​(a;θ) respectively. The primitive model appears in §2, p. 5.

Designer iii can send agent jjj a message in a finite set MijM_i^jMij​. The designer's full message profile is mi∈Mi=∏jMijm_i\in M_i=\prod_jM_i^jmi​∈Mi​=∏j​Mij​. A statistical experiment is a rule xi:Θi→Δ(Mi)x_i:\Theta_i\to\Delta(M_i)xi​:Θi​→Δ(Mi​): at each value of the designer's state component it specifies a lottery on full message profiles. The designers choose their experiments simultaneously from Xi=Δ(Mi)ΘiX_i=\Delta(M_i)^{\Theta_i}Xi​=Δ(Mi​)Θi​. Each agent observes the whole experiment profile x=(xi)ix=(x_i)_ix=(xi​)i​, but later sees only his own messages mj=(mij)im^j=(m_i^j)_imj=(mij​)i​. The signal ω\omegaω, uniformly distributed on [0,1][0,1][0,1], is public to all agents and may coordinate their action lotteries. Nature draws the state from p0p^0p0; conditional on it, each designer's experiment draws a message profile. This sequence is Definition 1 and the game timeline, pp. 5–6.

For fixed xxx, the agents play a Bayesian continuation game GM(x)G_M(x)GM​(x). Its equilibrium-outcome correspondence EM(x)⊆Δ(A)ME_M(x)\subseteq\Delta(A)^MEM​(x)⊆Δ(A)M consists of conditional distributions of action profiles given all messages that can arise from Bayes Nash behavior using the public signal. The agents' incentive test is ex ante: each agent may replace his entire private-message-to-action-lottery rule while the others keep theirs. Private messages with zero probability impose no incentive restriction. Public randomization allows convex combinations of equilibrium outcomes. As stated in §2, p. 6, EM(x)E_M(x)EM​(x) is nonempty and convex and is the set of public correlated equilibrium outcomes.

An equilibrium selection τ\tauτ chooses one τ(x)∈EM(x)\tau(x)\in E_M(x)τ(x)∈EM​(x) at every xxx and is Borel measurable. For a selected outcome, designer iii receives

Uiτ(x)=∑θ∈Θ∑m∈M∑a∈Ap0(θ)(∏i′∈Nxi′(mi′∣θi′))τ(x)(a∣m)ui(a;θ).U_i^\tau(x)=\sum_{\theta\in\Theta}\sum_{m\in M}\sum_{a\in A}p^0(\theta) \left(\prod_{i'\in N}x_{i'}(m_{i'}\mid\theta_{i'})\right) \tau(x)(a\mid m)u_i(a;\theta).Uiτ​(x)=θ∈Θ∑​m∈M∑​a∈A∑​p0(θ)(i′∈N∏​xi′​(mi′​∣θi′​))τ(x)(a∣m)ui​(a;θ).

The designers may themselves mix: designer iii chooses a Borel probability measure ζi\zeta_iζi​ on XiX_iXi​, and the profile law is the product of these measures. By Definition 2, p. 6, an equilibrium of GMG_MGM​ is a selection τ\tauτ together with a mixed Nash equilibrium ζ\zetaζ of the designers' induced game. Selection at every xxx gives the agents equilibrium behavior in every continuation subgame.

Formalization targets

The goal is Theorem 1, p. 6: for every finite profile of message sets, the game admits an equilibrium. With X=∏iXiX=\prod_iX_iX=∏i​Xi​ and U(x)={(Ui(x,y))i:y∈EM(x)}\mathcal U(x)=\{(U_i(x,y))_i:y\in E_M(x)\}U(x)={(Ui​(x,y))i​:y∈EM​(x)}, the statement is

∃ τ:X→Δ(A)M, ζ∈∏iΔ(Xi)such thatτ(x)∈EM(x) (∀x),τ is Borel measurable,ζ is a mixed Nash equilibrium for Uτ.\exists\,\tau:X\to\Delta(A)^M,\ \zeta\in\prod_i\Delta(X_i) \quad\text{such that}\quad \tau(x)\in E_M(x)\ (\forall x),\quad \tau\text{ is Borel measurable},\quad \zeta\text{ is a mixed Nash equilibrium for }U^\tau.∃τ:X→Δ(A)M, ζ∈i∏​Δ(Xi​)such thatτ(x)∈EM​(x) (∀x),τ is Borel measurable,ζ is a mixed Nash equilibrium for Uτ.

The milestone list follows the assertions used in the paper's proof. It first records the nonemptiness, convexity, and public-correlation characterization of EM(x)E_M(x)EM​(x); then the compact convex values and upper hemicontinuity of EME_MEM​ and U\mathcal UU. The final two milestones record a Borel measurable payoff selection U∗(x)∈U(x)U^*(x)\in\mathcal U(x)U∗(x)∈U(x) whose designers' game has a mixed equilibrium, and a Borel measurable continuation selection τ\tauτ that realizes that selected payoff. These statements are on pp. 6–7.

What the result establishes

Theorem 1 guarantees that the general finite-message model has at least one consistent profile of information design and agent continuation behavior. It permits multiple designers, arbitrary correlation in the prior, messages sent privately to different agents, and mixed experiment choices. The result supports the paper's later analysis by ensuring that the baseline game has an equilibrium even though an arbitrary continuation selection need not support one. The theorem is proved in the paper; the machine-checked proof and reusable formal infrastructure are the work of this mission.

Formalizing the result adds a precise interface for finite statistical experiments, agent continuation equilibria with public correlation, set-valued payoff maps, measurable selections, and mixed deviations over spaces of experiments. The finite-game lottery predicate from the published agt_games definition and its proved Nash-existence theorem are available as prerequisites. The remaining public-device, continuity, and selection material can serve other Bayesian-game formalizations.

Where the proof is difficult

The obvious first step is to choose one equilibrium outcome of the agents' game at each experiment profile and then seek a Nash equilibrium of the designers' game. That step fails because the chosen continuation outcome may change discontinuously with the experiments. In the paper's Example 5, a particular legitimate selection gives an induced zero-sum game with a gap between maxmin and minmax payoffs, so it has no approximate equilibrium. An argument that assumes every measurable selection works would be false. The existence theorem needs a selection compatible with mixed strategic behavior, and it needs that selection realized by actual agent equilibrium outcomes. Both demands are represented in the last two milestones.

Formalization scope

The Lean model uses finite nonempty designer, agent, state, action, and message types. Nonempty message sets are an explicit addition to the printed hypotheses: if one such set were empty, that designer would have no experiment and the existence claim would fail. The paper's printed action-product index NNN is read as KKK, the agent index, because actions belong to agents. The common prior is a lottery on the whole state profile, without a product assumption.

Finite lotteries are nonnegative real weight functions summing to one. The experiment spaces inherit the finite-dimensional product topology and Borel sigma algebra. The public signal has Lebesgue probability law on [0,1][0,1][0,1]; its strategy process is measurable and Bayes Nash at almost every signal value. The outcome correspondence is constructed from these processes, with no free outcome variable unrelated to agent incentives. Upper hemicontinuity uses the open-neighborhood definition. Designer mixed profiles are independent Borel probability measures, and deviations range over every such measure. Integrability is required for the mixed payoff and every deviating payoff, avoiding Lean's default value for a nonintegrable Bochner integral.

The target requires an existential Borel selection τ\tauτ that belongs to EM(x)E_M(x)EM​(x) at every xxx. A fixed selection, a nonmeasurable selection, or an unconstrained outcome map would alter the theorem. Contributions that establish compactness, upper hemicontinuity, measurable selection, and the mixed-equilibrium conclusion for these exact definitions are in scope. The separate results on splitting games, corporations, and multi-period play are outside this mission.

Selected references

  • F. Koessler, M. Laclau, and T. Tomala, Interactive Information Design, SSRN working paper 3171992, version of April 25, 2018; published in Mathematics of Operations Research, 2021. Working paper.
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