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Gaussian relative entropy variance

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QAlgorithms.Papers.Independent.Intakethird100.P17.gaussian_relative_entropy_variance

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

prove2quantum

Let n≥0n\ge0n≥0, κ>0\kappa>0κ>0, and let ρj=ρ(σj,uj)\rho_j=\rho(\sigma_j,u_j)ρj​=ρ(σj​,uj​), j=1,2j=1,2j=1,2, be normalized finite-energy Gaussian densities on the full nnn-mode oscillator space. The centered half-anticommutator covariance convention is [q,qT]=iΩ/κ[q,q^T]=i\Omega/\kappa[q,qT]=iΩ/κ, with canonical interleaved Ω\OmegaΩ. Assume every symplectic eigenvalue of each covariance exceeds 1/(2κ)1/(2\kappa)1/(2κ); both finite mean vectors are arbitrary. Write Gj=2iΩarcoth⁡(2κσjiΩ)G_j=2i\Omega\operatorname{arcoth}(2\kappa\sigma_j i\Omega)Gj​=2iΩarcoth(2κσj​iΩ), G~=G1−G2\widetilde G=G_1-G_2G=G1​−G2​, δ=u1−u2\delta=u_1-u_2δ=u1​−u2​, B=8κ2G2σ1G2B=8\kappa^2G_2\sigma_1G_2B=8κ2G2​σ1​G2​, and S=S(ρ1∥ρ2)S=S(\rho_1\Vert\rho_2)S=S(ρ1​∥ρ2​) in bits. Then SSS and the centered operator log-likelihood variance are finite, and

V(ρ1∥ρ2):=Tr⁡ ⁣[ρ1(log⁡2ρ1−log⁡2ρ2−S)2]=4κ2tr⁡(σ1G~σ1G~)+tr⁡(G~ΩG~Ω)+δTBδ2(2ln⁡2)2.\begin{aligned} V(\rho_1\Vert\rho_2) &:=\operatorname{Tr}\!\left[\rho_1(\log_2\rho_1-\log_2\rho_2-S)^2\right]\\ &=\frac{4\kappa^2\operatorname{tr}(\sigma_1\widetilde G\sigma_1\widetilde G)+\operatorname{tr}(\widetilde G\Omega\widetilde G\Omega)+\delta^TB\delta}{2(2\ln2)^2}. \end{aligned}V(ρ1​∥ρ2​)​:=Tr[ρ1​(log2​ρ1​−log2​ρ2​−S)2]=2(2ln2)24κ2tr(σ1​Gσ1​G)+tr(GΩGΩ)+δTBδ​.​

The positive sign before the term containing two copies of Ω\OmegaΩ is part of the formula. The variance vanishes for identical states.

Formalization Note The left side is the convergent density-weighted limit of the square of the actual centered operator log difference. The order of multiplication is retained; this is not a classical variance that assumes the operators commute.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P17
Formal statement
noncomputable section

open Filter

open scoped BigOperators ENNReal ComplexOrder Topology Matrix

attribute [local instance] Classical.propDecidable Classical.decEq

namespace QAlgorithms.Papers.Independent.Intakethird100.P17

open QAlgorithms.Papers.Independent.Claimed50.P30
  (ModeOperator Quadrature PhaseVector numberKet trace TraceSummable
   FockWeylSystem GaussianMoments canonicalOmega)

open QAlgorithms.Papers.Independent.Dependency50.P13 (IsTraceClass relativeEntropy)

open QAlgorithms.Papers.Independent.Next46.P17 (raisingCoefficient)

/-- arXiv:1808.00608v4, Appendix A, PDF p.8, final unnumbered formula.
Variance of the actual centered NONCOMMUTATIVE operator log difference.
Both covariances have finite Gibbs matrices; all means are allowed. -/
theorem gaussian_relative_entropy_variance {n : ℕ}
    (W : FockWeylSystem n) (kappa : ℝ) (hkappa : 0 < kappa)
    (rho1 rho2 : ModeOperator n) (u1 u2 : PhaseVector n)
    (sigma1 sigma2 : Matrix (Quadrature n) (Quadrature n) ℝ)
    (h1 : GaussianInConvention W kappa rho1 u1 sigma1)
    (h2 : GaussianInConvention W kappa rho2 u2 sigma2)
    (hs1 : StrictGibbsCovariance kappa sigma1)
    (hs2 : StrictGibbsCovariance kappa sigma2) :
    let D := oscillatorRelativeEntropy rho1 rho2 h1.1.1.1 h2.1.1.1
    let expression := gaussianVarianceExpression kappa sigma1 sigma2 (u1 - u2)
    Summable (gibbsSeriesTerm kappa sigma1) ∧
    Summable (gibbsSeriesTerm kappa sigma2) ∧ D < ⊤ ∧
    (∀ k, let L := oscillatorLogCutoff rho1 k - oscillatorLogCutoff rho2 k -
        (D.toReal : ℂ) • (1 : ModeOperator n)
      TraceSummable (rho1.comp (L.comp L))) ∧
    Tendsto (relativeVarianceCutoff rho1 rho2 D.toReal) atTop (nhds expression) ∧
    oscillatorRelativeVariance rho1 rho2 D.toReal = expression := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P17

end
Source
Tight bounds for private communication over bosonic Gaussian channels based on teleportation simulation with optimal finite resources, arXiv:1808.00608v4, PDF p. 8, Appendix A final unnumbered formula: relative entropy variance
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What the Lean code literally says, in plain math · gpt-6.1-sol

Let nnn be any natural number, including 000, let H=ℓ2(Nn;C)\mathcal H=\ell^2(\mathbb N^n;\mathbb C)H=ℓ2(Nn;C), and write ∣j⟩|j\rangle∣j⟩ for its standard basis vector indexed by j∈Nnj\in\mathbb N^nj∈Nn. All operators below are bounded complex-linear operators on H\mathcal HH, and their specified trace is tr⁡T=∑j⟨j∣T∣j⟩\operatorname{tr}T=\sum_j\langle j|T|j\rangletrT=∑j​⟨j∣T∣j⟩; “trace summable” means that this complex diagonal series is summable. Here an operator TTT is called trace class precisely when there are sequences ar,br∈Ha_r,b_r\in\mathcal Har​,br​∈H with ∑r∥ar∥∥br∥<∞\sum_r\|a_r\|\|b_r\|<\infty∑r​∥ar​∥∥br​∥<∞ and Tx=∑r⟨br,x⟩arTx=\sum_r\langle b_r,x\rangle a_rTx=∑r​⟨br​,x⟩ar​ for every x∈Hx\in\mathcal Hx∈H; positivity means T∗=TT^*=TT∗=T and Re⁡⟨x,Tx⟩≥0\operatorname{Re}\langle x,Tx\rangle\ge0Re⟨x,Tx⟩≥0 for every xxx. Index phase coordinates by (k,a)∈{0,…,n−1}×{0,1}(k,a)\in\{0,\ldots,n-1\}\times\{0,1\}(k,a)∈{0,…,n−1}×{0,1}, and let Ω\OmegaΩ have entries Ω(k,0),(k,1)=1\Omega_{(k,0),(k,1)}=1Ω(k,0),(k,1)​=1, Ω(k,1),(k,0)=−1\Omega_{(k,1),(k,0)}=-1Ω(k,1),(k,0)​=−1, and all other entries zero. Fix a system WWW assigning a unitary operator W(ξ)W(\xi)W(ξ) to every real phase vector ξ\xiξ, with number-basis coefficients

[W(ξ)]ij=∏k=0n−1dikjk(−ξk,1+iξk,0),dab(z)=e−∣z∣2/2a! b!∑r=0min⁡(a,b)za−r(−z‾)b−rr!(a−r)!(b−r)!.[W(\xi)]_{ij}=\prod_{k=0}^{n-1}d_{i_kj_k}(-\xi_{k,1}+\mathrm i\xi_{k,0}),\qquad d_{ab}(z)=e^{-|z|^2/2}\sqrt{a!\,b!} \sum_{r=0}^{\min(a,b)} \frac{z^{a-r}(-\overline z)^{b-r}}{r!(a-r)!(b-r)!}.[W(ξ)]ij​=k=0∏n−1​dik​jk​​(−ξk,1​+iξk,0​),dab​(z)=e−∣z∣2/2a!b!​r=0∑min(a,b)​r!(a−r)!(b−r)!za−r(−z)b−r​.

For every real κ>0\kappa>0κ>0, every pair of operators ρ1,ρ2\rho_1,\rho_2ρ1​,ρ2​, real phase vectors u1,u2u_1,u_2u1​,u2​, and real phase-coordinate matrices σ1,σ2\sigma_1,\sigma_2σ1​,σ2​, assume, for each i=1,2i=1,2i=1,2, that ρi\rho_iρi​ is positive and trace class, is trace summable with trace 111, and satisfies the finite-energy summability condition

∑j∈Nn(∑m=0n−1jm)Re⁡⟨j∣ρi∣j⟩<∞.\sum_{j\in\mathbb N^n}\left(\sum_{m=0}^{n-1}j_m\right) \operatorname{Re}\langle j|\rho_i|j\rangle<\infty.j∈Nn∑​(m=0∑n−1​jm​)Re⟨j∣ρi​∣j⟩<∞.

Assume also that 2κσi2\kappa\sigma_i2κσi​ is symmetric and positive definite, that 2κσi+iΩ2\kappa\sigma_i+\mathrm i\Omega2κσi​+iΩ is positive semidefinite, and that, for every real phase vector ξ\xiξ, ρiW(ξ)\rho_iW(\xi)ρi​W(ξ) is trace summable and

tr⁡(ρiW(ξ))=exp⁡ ⁣(−κ ξTσiξ+i2κ uiTξ).\operatorname{tr}(\rho_iW(\xi)) =\exp\!\left(-\kappa\,\xi^{\mathsf T}\sigma_i\xi +\mathrm i\sqrt{2\kappa}\,u_i^{\mathsf T}\xi\right).tr(ρi​W(ξ))=exp(−κξTσi​ξ+i2κ​uiT​ξ).

The further strict covariance hypotheses require σiT=σi\sigma_i^{\mathsf T}=\sigma_iσiT​=σi​, σi\sigma_iσi​ positive definite, and κσi+i2Ω\kappa\sigma_i+\frac{\mathrm i}{2}\Omegaκσi​+2i​Ω positive definite as a complex matrix. Define D∈[0,∞]D\in[0,\infty]D∈[0,∞] as follows: for every m∈Nm\in\mathbb Nm∈N and every orthonormal family v0,…,vm−1∈Hv_0,\ldots,v_{m-1}\in\mathcal Hv0​,…,vm−1​∈H, form the compressed matrices Xab=⟨va,ρ1vb⟩X_{ab}=\langle v_a,\rho_1v_b\rangleXab​=⟨va​,ρ1​vb​⟩ and Yab=⟨va,ρ2vb⟩Y_{ab}=\langle v_a,\rho_2v_b\rangleYab​=⟨va​,ρ2​vb​⟩; assign this pair the value

R(X,Y)={max⁡ ⁣{0,Re⁡Tr⁡(f(X)−Xlog⁡Y−X+Y)},ker⁡Y⊆ker⁡X,+∞,otherwise,f(x)=xlog⁡x,D=sup⁡m,vR(X,Y)log⁡2.\mathcal R(X,Y)= \begin{cases} \max\!\left\{0,\operatorname{Re}\operatorname{Tr} \bigl(f(X)-X\log Y-X+Y\bigr)\right\}, &\ker Y\subseteq\ker X,\\ +\infty,&\text{otherwise}, \end{cases} \qquad f(x)=x\log x, \quad D=\frac{\sup_{m,v}\mathcal R(X,Y)}{\log2}.R(X,Y)={max{0,ReTr(f(X)−XlogY−X+Y)},+∞,​kerY⊆kerX,otherwise,​f(x)=xlogx,D=log2supm,v​R(X,Y)​.

The matrix functions here use continuous functional calculus and the total real logarithm, for which log⁡0=0\log0=0log0=0; the supremum includes the empty orthonormal family m=0m=0m=0. Regarding real matrices as complex matrices when needed, define

Bi=2κ σi(iΩ),Ti,r=(Bi−1)2r+12r+1,Gi=Re⁡ ⁣(2i Ω∑r=0∞Ti,r),B_i=2\kappa\,\sigma_i(\mathrm i\Omega),\qquad T_{i,r}=\frac{(B_i^{-1})^{2r+1}}{2r+1},\qquad G_i=\operatorname{Re}\!\left(2\mathrm i\,\Omega\sum_{r=0}^{\infty}T_{i,r}\right),Bi​=2κσi​(iΩ),Ti,r​=2r+1(Bi−1​)2r+1​,Gi​=Re(2iΩr=0∑∞​Ti,r​),

where the real part is taken entrywise and matrix inverses are the total matrix inverse, equal to zero for a singular matrix. Put δ=u1−u2\delta=u_1-u_2δ=u1​−u2​, ΔG=G1−G2\Delta G=G_1-G_2ΔG=G1​−G2​, and

V=4κ2Tr⁡(σ1ΔG σ1ΔG)+Tr⁡(ΔG ΩΔG Ω)+δT(8κ2G2σ1G2)δ2(2log⁡2)2.V= \frac{ 4\kappa^2\operatorname{Tr}(\sigma_1\Delta G\,\sigma_1\Delta G) +\operatorname{Tr}(\Delta G\,\Omega\Delta G\,\Omega) +\delta^{\mathsf T}(8\kappa^2G_2\sigma_1G_2)\delta }{ 2(2\log2)^2 }.V=2(2log2)24κ2Tr(σ1​ΔGσ1​ΔG)+Tr(ΔGΩΔGΩ)+δT(8κ2G2​σ1​G2​)δ​.

For k∈Nk\in\mathbb Nk∈N, let

ℓk(ρ)=[x⟼log⁡max⁡{x,1/(k+1)}log⁡2](ρ),Lk=ℓk(ρ1)−ℓk(ρ2)−DRI,\ell_k(\rho)= \left[x\longmapsto\frac{\log\max\{x,1/(k+1)\}}{\log2}\right](\rho), \qquad L_k=\ell_k(\rho_1)-\ell_k(\rho_2)-D_{\mathbb R}I,ℓk​(ρ)=[x⟼log2logmax{x,1/(k+1)}​](ρ),Lk​=ℓk​(ρ1​)−ℓk​(ρ2​)−DR​I,

using continuous functional calculus, where DRD_{\mathbb R}DR​ is the real-value conversion of DDD (this conversion sends +∞+\infty+∞ to zero). Then both matrix series ∑rT1,r\sum_rT_{1,r}∑r​T1,r​ and ∑rT2,r\sum_rT_{2,r}∑r​T2,r​ are summable, D<+∞D<+\inftyD<+∞, every operator ρ1Lk2\rho_1L_k^2ρ1​Lk2​ is trace summable, and

lim⁡k→∞Re⁡tr⁡(ρ1Lk2)=V.\lim_{k\to\infty}\operatorname{Re}\operatorname{tr}(\rho_1L_k^2)=V.k→∞lim​Retr(ρ1​Lk2​)=V.

Moreover, the quantity defined by applying the general filter-limit operator to this same sequence, namely the specified oscillator relative variance with mean DRD_{\mathbb R}DR​, equals VVV. No positive-mode hypothesis is imposed: n=0n=0n=0 is included, with empty phase-coordinate matrices and empty phase-coordinate sums.

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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