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A completely positive trace-decreasing map can increase relative entropy

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QAlgorithms.Papers.Independent.Intakethird100.P46.trace_decrease_counterexample

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

prove2quantum

On complex 2×22\times22×2 matrices define Φ(vwxy)=diag⁡(v/2,y)\Phi\left(\begin{smallmatrix}v&w\\x&y\end{smallmatrix}\right)=\operatorname{diag}(v/2,y)Φ(vx​wy​)=diag(v/2,y) and fix the density matrices ρ=diag⁡(1/3,2/3)\rho=\operatorname{diag}(1/3,2/3)ρ=diag(1/3,2/3) and σ=diag⁡(2/3,1/3)\sigma=\operatorname{diag}(2/3,1/3)σ=diag(2/3,1/3). The map is complex-linear, completely positive, and trace-nonincreasing on every positive matrix. Nevertheless, for literal unnormalized relative entropy in natural units,

D(ρ∥σ)=log⁡23<log⁡22=D(Φ(ρ)∥Φ(σ)).D(\rho\Vert\sigma)=\frac{\log2}{3}<\frac{\log2}{2}=D(\Phi(\rho)\Vert\Phi(\sigma)).D(ρ∥σ)=3log2​<2log2​=D(Φ(ρ)∥Φ(σ)).

The output operators have traces 5/65/65/6 and 2/32/32/3 and are not asserted to be normalized. This counterexample explains why trace nonincrease alone does not suffice for the Umegaki data-processing inequality.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P46
Formal statement
noncomputable section

open scoped Matrix ComplexConjugate ComplexOrder ENNReal

namespace QAlgorithms.Papers.Independent.Intakethird100.P46

attribute [local instance] Classical.propDecidable Classical.decEq

open QAlgorithms

open QAlgorithms.Papers.Independent.Dependency50.P13
  (IsTraceClass TraceClass IsPositive operatorTrace)

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

open QAlgorithms.Papers.Independent.Bridge50.P49 (beigiSandwichedDivergence)

variable {H K : Type*}
  [NormedAddCommGroup H] [InnerProductSpace ℂ H] [CompleteSpace H]
  [NormedAddCommGroup K] [InnerProductSpace ℂ K] [CompleteSpace K]

/-- Müller-Hermes–Reeb, arXiv:1512.06117v3, PDF p.3, unnumbered counterexample.
Both INPUTS are density matrices. The CP trace-nonincreasing map produces
subnormalized operators, and the two literal relative entropies increase strictly. -/
theorem trace_decrease_counterexample :
    Entanglement.IsCompletelyPositive traceDecreaseMap ∧
    PositiveMatrixTraceNonincreasing traceDecreaseMap ∧
    IsDensityMatrix counterexampleRho ∧ IsDensityMatrix counterexampleSigma ∧
    finiteLiteralRelativeEntropy counterexampleRho counterexampleSigma =
      ((Real.log 2 / 3 : ℝ) : EReal) ∧
    ((Real.log 2 / 3 : ℝ) : EReal) < ((Real.log 2 / 2 : ℝ) : EReal) ∧
    finiteLiteralRelativeEntropy (traceDecreaseMap counterexampleRho)
      (traceDecreaseMap counterexampleSigma) = ((Real.log 2 / 2 : ℝ) : EReal) := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P46

end
Source
Monotonicity of the Quantum Relative Entropy Under Positive Maps, arXiv:1512.06117v3, PDF p. 3, Unnumbered counterexample: trace nonincrease alone does not give relative-entropy monotonicity
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What the Lean code literally says, in plain math · gpt-6.1-sol

Define the complex linear map on 2×22\times22×2 matrices and the two matrices

Φ(X)=(X00/200X11),ρ=(1/3002/3),σ=(2/3001/3).\Phi(X)= \begin{pmatrix}X_{00}/2&0\\0&X_{11}\end{pmatrix}, \qquad \rho=\begin{pmatrix}1/3&0\\0&2/3\end{pmatrix}, \qquad \sigma=\begin{pmatrix}2/3&0\\0&1/3\end{pmatrix}.Φ(X)=(X00​/20​0X11​​),ρ=(1/30​02/3​),σ=(2/30​01/3​).

For finite matrices, define the extended-real quantity

Dlit(A,B)={Re⁡tr⁡ ⁣(f(A)−Alog⁡B),ker⁡B⊆ker⁡A,+∞,otherwise,f(t)=tlog⁡t,D_{\mathrm{lit}}(A,B)= \begin{cases} \operatorname{Re}\operatorname{tr}\!\left(f(A)-A\log B\right), &\ker B\subseteq\ker A,\\ +\infty,&\text{otherwise}, \end{cases} \qquad f(t)=t\log t,Dlit​(A,B)={Retr(f(A)−AlogB),+∞,​kerB⊆kerA,otherwise,​f(t)=tlogt,

using continuous functional calculus and the natural logarithm, with log⁡0=0\log0=0log0=0. The declaration asserts all of the following: Φ\PhiΦ is completely positive, meaning that for every natural number rrr, including r=0r=0r=0, applying Φ\PhiΦ to every 2×22\times22×2 block of any positive semidefinite matrix indexed by {0,…,r−1}×{0,1}\{0,\ldots,r-1\}\times\{0,1\}{0,…,r−1}×{0,1} gives a positive semidefinite matrix; for every positive semidefinite AAA, Φ(A)\Phi(A)Φ(A) is positive semidefinite and Re⁡tr⁡Φ(A)≤Re⁡tr⁡A\operatorname{Re}\operatorname{tr}\Phi(A)\le\operatorname{Re}\operatorname{tr}ARetrΦ(A)≤RetrA; both ρ\rhoρ and σ\sigmaσ are positive semidefinite with complex trace one; and

Dlit(ρ,σ)=log⁡23<log⁡22=Dlit(Φ(ρ),Φ(σ)).D_{\mathrm{lit}}(\rho,\sigma)=\frac{\log2}{3} <\frac{\log2}{2} =D_{\mathrm{lit}}(\Phi(\rho),\Phi(\sigma)).Dlit​(ρ,σ)=3log2​<2log2​=Dlit​(Φ(ρ),Φ(σ)).
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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