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Lemma 11 / Eqs.(84)–(86): permutation-covariant de Finetti smoothing

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QAlgorithms.Papers.Independent.Intakethird100.P65.deFinetti_smoothing

by Gohan · Nov 1, 2026 · Mathlib 0df444a (Lean v4.33.1)

prove2quantum

All systems have finite positive dimension and fixed computational bases. Matrix order is Loewner order and logarithms are base two.

Let N:A′→B\mathcal N:A'\to BN:A′→B be CPTP, n≥1n\ge1n≥1, and 0<ε<10<\varepsilon<10<ε<1, with AAA a copy of A′A'A′. Let ω(AA′)n=∫σAA′⊗n dσ\omega_{(AA')^n}=\int\sigma_{AA'}^{\otimes n}\,d\sigmaω(AA′)n​=∫σAA′⊗n​dσ be the Haar pure-state moment, constructed as the normalized symmetric projector, and let ΩRAnA′n\Omega_{RA^nA'^n}ΩRAnA′n​ be its canonical purification. Set τ=(idRAn⊗N⊗n)(Ω)\tau=(\mathrm{id}_{RA^n}\otimes\mathcal N^{\otimes n})(\Omega)τ=(idRAn​⊗N⊗n)(Ω). Let K1K_1K1​ consist of density matrices σRAnBn\sigma_{RA^nB^n}σRAnBn​ with P(σ,τ)≤εP(\sigma,\tau)\le\varepsilonP(σ,τ)≤ε and σRAn=ΩRAn\sigma_{RA^n}=\Omega_{RA^n}σRAn​=ΩRAn​. Let K2K_2K2​ consist of outputs (idRAn⊗N~)(Ω)(\mathrm{id}_{RA^n}\otimes\widetilde{\mathcal N})(\Omega)(idRAn​⊗N)(Ω) at distance at most ε\varepsilonε from τ\tauτ, with N~\widetilde{\mathcal N}N a permutation-covariant CPTP channel. Then

inf⁡σ∈K1Imax⁡(RAn:Bn)σ=inf⁡σ∈K2Imax⁡(RAn:Bn)σ.\inf_{\sigma\in K_1}I_{\max}(RA^n:B^n)_\sigma=\inf_{\sigma\in K_2}I_{\max}(RA^n:B^n)_\sigma.σ∈K1​inf​Imax​(RAn:Bn)σ​=σ∈K2​inf​Imax​(RAn:Bn)σ​.

Covariance means N~∘πA′n=πBn∘N~\widetilde{\mathcal N}\circ\pi_{A'^n}=\pi_{B^n}\circ\widetilde{\mathcal N}N∘πA′n​=πBn​∘N. This explicitly repairs the source's same-forward-permutation notation; the source proof requires inverse input conjugation. The round-one moderator independently checked this minimal inverse-permutation repair; fresh read-backs remain necessary after the other round-one edits.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P65
Formal statement
noncomputable section

open Filter QAlgorithms

open scoped BigOperators Matrix ComplexConjugate ComplexOrder Topology

namespace QAlgorithms.Papers.Independent.Intakethird100.P65

attribute [local instance] Classical.propDecidable Classical.decEq

open QAlgorithms.Papers.Independent.Dependency50.P09 (transposeBob)

open QAlgorithms.Papers.Independent.Dependency50.P35 (maxRelative)

open QAlgorithms.Papers.Independent.Independent2.P01 (dampingKraus damping)

open QAlgorithms.Papers.Independent.Gap48.P33 (phaseFlipChannel)

open QAlgorithms.Papers.Independent.Information100.P06 (Below)

open QAlgorithms.Papers.Independent.Information100.P34 (choi)

open QAlgorithms.Papers.Independent.Information100.P62 (Stochastic)

open QAlgorithms.Papers.Independent.Information100.P63 (Mat)

open QAlgorithms.Papers.Independent.Information100.P67 (IsSubnormalized purifiedDistance)

open QAlgorithms.Papers.Independent.Information100.P69 (replacer permute)

open QAlgorithms.Papers.Independent.Information100.P71 (diamond channelPower channelRelative)

open QAlgorithms.Papers.Independent.Support50.P39 (quantumMutualInformation)

variable {A B C D R : Type} [Fintype A] [DecidableEq A]
  [Fintype B] [DecidableEq B] [Fintype C] [DecidableEq C]
  [Fintype D] [DecidableEq D] [Fintype R] [DecidableEq R]

variable [Nonempty A] [Nonempty B] [Nonempty C] [Nonempty D] [Nonempty R]

/-- Lemma 11, Eqs.(84)--(86), PDF p.20. The full R A^n reference is fixed.
SOURCE NOTATION REPAIR: Perm is interpreted as covariance (inverse input
conjugation), as required by tensor-power invariance in the printed proof;
no arbitrary-superchannel monotonicity is assumed. The independent round-one
moderator checked that this only repairs the inverse in the permutation action. -/
theorem deFinetti_smoothing (N : Superop A B) (hN : IsChannel N)
    (n : ℕ) (hn : 0 < n) (e : ℝ) (h0 : 0 < e) (h1 : e < 1) :
    infoOnSet (deFinettiK1 N n e) = infoOnSet (deFinettiK2 N n e) := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P65

end
Source
Quantum Channel Simulation and the Channel's Smooth Max-Information, arXiv:1807.05354v2, PDF p. 20, Lemma 11 / Eqs.(84)–(86): permutation-covariant de Finetti smoothing
Read-back

What the Lean code literally says, in plain math · gpt-6.1-sol

For finite types A,BA,BA,B with decidable equality, every channel N:Mat⁡(A)→Mat⁡(B)N:\operatorname{Mat}(A)\to\operatorname{Mat}(B)N:Mat(A)→Mat(B), every positive integer nnn, and every real 0<e<10<e<10<e<1, put d=∣A∣d=|A|d=∣A∣ and define a matrix Ωn\Omega_nΩn​ on (A×A)n(A\times A)^n(A×A)n by

Ωn(x,y)=∑π∈Sym⁡(n)1{x=y∘π}n!(d2+n−1n).\Omega_n(x,y)= \frac{\sum_{\pi\in\operatorname{Sym}(n)}\mathbf1_{\{x=y\circ\pi\}}} {n!\binom{d^2+n-1}{n}}.Ωn​(x,y)=n!(nd2+n−1​)∑π∈Sym(n)​1{x=y∘π}​​.

The subtraction in the binomial coefficient is natural-number subtraction, truncated at zero. Define a vector vvv on Rn×An\mathcal R_n\times A^nRn​×An, where Rn=(A×A)n×An\mathcal R_n=(A\times A)^n\times A^nRn​=(A×A)n×An, by v((z,u),a)=(Ωn)z, (ui,ai)iv((z,u),a)=(\sqrt{\Omega_n})_{z,\,(u_i,a_i)_i}v((z,u),a)=(Ωn​​)z,(ui​,ai​)i​​, let Ξn=vv†\Xi_n=vv^\daggerΞn​=vv†, and let ωN=(idRn⊗N⊗n)(Ξn)\omega_N=(\mathrm{id}_{\mathcal R_n}\otimes N^{\otimes n})(\Xi_n)ωN​=(idRn​​⊗N⊗n)(Ξn​). Let K1K_1K1​ consist of density matrices σ\sigmaσ on Rn×Bn\mathcal R_n\times B^nRn​×Bn satisfying P(σ,ωN)≤eP(\sigma,\omega_N)\le eP(σ,ωN​)≤e and Tr⁡Bnσ=Tr⁡AnΞn\operatorname{Tr}_{B^n}\sigma=\operatorname{Tr}_{A^n}\Xi_nTrBn​σ=TrAn​Ξn​. Let K2K_2K2​ consist of matrices (idRn⊗T)(Ξn)(\mathrm{id}_{\mathcal R_n}\otimes T)(\Xi_n)(idRn​​⊗T)(Ξn​), where T:Mat⁡(An)→Mat⁡(Bn)T:\operatorname{Mat}(A^n)\to\operatorname{Mat}(B^n)T:Mat(An)→Mat(Bn) is a channel satisfying T(ΠπX)=Ππ(TX)T(\Pi_\pi X)=\Pi_\pi(TX)T(Ππ​X)=Ππ​(TX) for every permutation π\piπ and every matrix XXX, with (ΠπX)(x,y)=X(x∘π,y∘π)(\Pi_\pi X)(x,y)=X(x\circ\pi,y\circ\pi)(Ππ​X)(x,y)=X(x∘π,y∘π), and whose resulting matrix has distance at most eee from ωN\omega_NωN​. Then

inf⁡σ∈K1Imax⁡(σ)=inf⁡σ∈K2Imax⁡(σ),Imax⁡(X)=inf⁡τ⪰0, tr⁡τ=1inf⁡{ℓ∈R:2ℓ(XRn⊗τ)−X⪰0},\inf_{\sigma\in K_1}I_{\max}(\sigma)=\inf_{\sigma\in K_2}I_{\max}(\sigma), \quad I_{\max}(X)=\inf_{\tau\succeq0,\ \operatorname{tr}\tau=1} \inf\{\ell\in\mathbb R:2^\ell(X_{\mathcal R_n}\otimes\tau)-X\succeq0\},σ∈K1​inf​Imax​(σ)=σ∈K2​inf​Imax​(σ),Imax​(X)=τ⪰0, trτ=1inf​inf{ℓ∈R:2ℓ(XRn​​⊗τ)−X⪰0},

where XRn=Tr⁡BnXX_{\mathcal R_n}=\operatorname{Tr}_{B^n}XXRn​​=TrBn​X and all these infima are extended-real infima. A density matrix is positive semidefinite with trace 111; channels are finite Kraus maps with ∑jKj†Kj=I\sum_jK_j^\dagger K_j=I∑j​Kj†​Kj​=I, and tensor powers are defined by products of their matrix-unit entries. The distance is P(X,Y)=1−(Fr(X,Y)+(1−Re⁡tr⁡X)(1−Re⁡tr⁡Y))2P(X,Y)=\sqrt{1-(F_{\mathrm r}(X,Y)+\sqrt{(1-\operatorname{Re}\operatorname{tr}X)(1-\operatorname{Re}\operatorname{tr}Y)})^2}P(X,Y)=1−(Fr​(X,Y)+(1−RetrX)(1−RetrY)​)2​, where Fr(X,Y)=Re⁡tr⁡X YXF_{\mathrm r}(X,Y)=\operatorname{Re}\operatorname{tr}\sqrt{\sqrt X\,Y\sqrt X}Fr​(X,Y)=RetrX​YX​​. Matrix square roots use continuous functional calculus, division by zero is 000, and empty feasible sets have infimum +∞+\infty+∞. No nonemptiness condition on A,BA,BA,B is displayed. The conclusion equates the infimum values, without asserting equality of K1K_1K1​ and K2K_2K2​ or attainment.

Human review
  • Endorsed by Shuze Chen · Nov 1, 2026

    Confirmed by the moderator at approval.

  • Endorsed by Gohan · Nov 1, 2026

    Confirmed by the mission captain (proposal self-audit).

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