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Lemma 12 / Eq.(97): fixed-marginal mutual-information continuity

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QAlgorithms.Papers.Independent.Intakethird100.P65.fixedMarginal_continuity

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 ρAB,σAB\rho_{AB},\sigma_{AB}ρAB​,σAB​ be normalized density matrices with ρA=σA\rho_A=\sigma_AρA​=σA​, and let ε≥0\varepsilon\ge0ε≥0 satisfy 12∥ρ−σ∥1≤ε\tfrac12\|\rho-\sigma\|_1\le\varepsilon21​∥ρ−σ∥1​≤ε. Then

∣I(A:B)ρ−I(A:B)σ∣≤2εlog⁡2∣A∣+(1+ε)h2 ⁣(ε1+ε).|I(A:B)_\rho-I(A:B)_\sigma|\le2\varepsilon\log_2|A|+(1+\varepsilon)h_2\!\left(\frac{\varepsilon}{1+\varepsilon}\right).∣I(A:B)ρ​−I(A:B)σ​∣≤2εlog2​∣A∣+(1+ε)h2​(1+εε​).

Here h2h_2h2​ is binary entropy in bits. The dimension is that of the fixed reference system AAA, and singular states are permitted.

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 12, Eq.(97), PDF p.21: fixed A marginal, half trace distance,
log dimension of A, and epsilon/(1+epsilon). -/
theorem fixedMarginal_continuity (rho sigma : Mat (A × B))
    (hrho : IsDensityMatrix rho) (hsigma : IsDensityMatrix sigma)
    (hmarg : traceRight rho = traceRight sigma) (e : ℝ) (he : 0 ≤ e)
    (hdist : traceNorm (rho - sigma) / 2 ≤ e) :
    |quantumMutualInformation rho - quantumMutualInformation sigma| ≤
      2*e*Real.logb 2 (Fintype.card A) + (1+e)*binEntropy2 (e/(1+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. 21, Lemma 12 / Eq.(97): fixed-marginal mutual-information continuity
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What the Lean code literally says, in plain math · gpt-6.1-sol

For finite types A,BA,BA,B with decidable equality, let ρ,σ\rho,\sigmaρ,σ be positive semidefinite matrices on A×BA\times BA×B, each of trace 111, whose AAA-marginals agree: Tr⁡Bρ=Tr⁡Bσ\operatorname{Tr}_B\rho=\operatorname{Tr}_B\sigmaTrB​ρ=TrB​σ. For every real e≥0e\ge0e≥0 satisfying 12∥ρ−σ∥1≤e\tfrac12\|\rho-\sigma\|_1\le e21​∥ρ−σ∥1​≤e, the assertion is

∣I(ρ)−I(σ)∣≤2elog⁡2∣A∣+(1+e) h2 ⁣(e1+e).\left|I(\rho)-I(\sigma)\right| \le 2e\log_2|A|+(1+e)\,h_2\!\left(\frac{e}{1+e}\right).∣I(ρ)−I(σ)∣≤2elog2​∣A∣+(1+e)h2​(1+ee​).

Here I(X)=S(Tr⁡BX)+S(Tr⁡AX)−S(X)I(X)=S(\operatorname{Tr}_B X)+S(\operatorname{Tr}_A X)-S(X)I(X)=S(TrB​X)+S(TrA​X)−S(X), S(X)=−Re⁡tr⁡f(X)S(X)=-\operatorname{Re}\operatorname{tr}f(X)S(X)=−Retrf(X) with f(x)=xlog⁡2xf(x)=x\log_2xf(x)=xlog2​x applied by continuous functional calculus, and h2(t)=binEntropy⁡(t)/log⁡2h_2(t)=\operatorname{binEntropy}(t)/\log2h2​(t)=binEntropy(t)/log2, where binEntropy⁡(t)=−tlog⁡t−(1−t)log⁡(1−t)\operatorname{binEntropy}(t)=-t\log t-(1-t)\log(1-t)binEntropy(t)=−tlogt−(1−t)log(1−t) uses the total real logarithm, including log⁡0=0\log0=0log0=0. The trace norm is ∥X∥1=Re⁡tr⁡X†X\|X\|_1=\operatorname{Re}\operatorname{tr}\sqrt{X^\dagger X}∥X∥1​=RetrX†X​, and each partial trace sums over the discarded diagonal index. The bound depends on ∣A∣|A|∣A∣; there is no upper restriction on eee, and e=0e=0e=0 is included. If either register is empty, the density hypotheses are impossible.

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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