Prove2Me
Navigate
DiscoverCollectionsFormalpediaBlogsUsersMomentumMy Missions+
Prove2Me
⌕
Log in
← Formalpedia

Quantitative continuity of relative entropy of entanglement

Open
QAlgorithms.Papers.Independent.Intakethird100.P100.relativeEntanglement_continuity

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

prove2quantum

Let HA,HBH_A,H_BHA​,HB​ be fixed complex Hilbert spaces of positive finite dimensions, and let N=dim⁡(HA⊗HB)N=\dim(H_A\otimes H_B)N=dim(HA​⊗HB​). For a bipartite state ω\omegaω, define Er(ω)=inf⁡τ∈SEPS(ω∥τ)E_r(\omega)=\inf_{\tau\in\mathrm{SEP}}S(\omega\Vert\tau)Er​(ω)=infτ∈SEP​S(ω∥τ), where SEP\mathrm{SEP}SEP consists exactly of finite convex combinations of product density matrices and SSS is Umegaki relative entropy, infinite when support inclusion fails.

All logarithms are natural. States are positive semidefinite matrices of trace one, and the distance t=∥σ−ρ∥1t=\|\sigma-\rho\|_1t=∥σ−ρ∥1​ uses the full trace norm, without a factor of one half. Set η(t)=−tlog⁡t\eta(t)=-t\log tη(t)=−tlogt, with η(0)=0\eta(0)=0η(0)=0.

For any states σ,ρ\sigma,\rhoσ,ρ with t≤1/3t\leq 1/3t≤1/3, both entanglement values are finite and

∣Er(σ)−Er(ρ)∣≤2(tlog⁡N+η(t))+4t.|E_r(\sigma)-E_r(\rho)|\leq 2\bigl(t\log N+\eta(t)\bigr)+4t.∣Er​(σ)−Er​(ρ)∣≤2(tlogN+η(t))+4t.

This is the precise separable-state specialization of the paper's Fannes-type continuity requirement. No separability assumption is imposed on either input.

Formalization Note The accepted entropy infimum is stored in bits. The Lean bound multiplies its absolute real difference by log⁡2\log 2log2, which gives exactly the natural-logarithm quantity displayed above.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P100
Formal statement
noncomputable section

open Filter

open scoped ENNReal Topology ComplexOrder

attribute [local instance] Classical.propDecidable Classical.decEq

namespace QAlgorithms.Papers.Independent.Intakethird100.P100

open QAlgorithms.Papers.Independent.Dependency50.P15
  (relativeDistance relativeDistanceValue positiveOperatorRelativeEntropyBits groupedTensorPower)

open QAlgorithms.Papers.Independent.Information100.P54
  (relativeEntropyBits relativeEntanglement)

/-- Introduction (2), PDF p.1, specialized via Remark (i), PDF p.2:
relative entropy of entanglement obeys the exact quantitative bound (3).
The local domain t <= 1/3 is retained. No separability of the inputs is assumed. -/
theorem relativeEntanglement_continuity
    {A B : Type*} [Fintype A] [Fintype B] [DecidableEq A] [DecidableEq B]
    [Nonempty A] [Nonempty B]
    (sigma rho : Matrix (A × B) (A × B) ℂ)
    (hsigma : IsDensityMatrix sigma) (hrho : IsDensityMatrix rho)
    (hclose : traceNorm (sigma - rho) ≤ 1 / 3) :
    relativeEntanglement sigma < ⊤ ∧ relativeEntanglement rho < ⊤ ∧
    Real.log 2 * |(relativeEntanglement sigma).toReal -
      (relativeEntanglement rho).toReal| ≤
      2 * (traceNorm (sigma - rho) * Real.log (Fintype.card (A × B)) +
        Real.negMulLog (traceNorm (sigma - rho))) + 4 * traceNorm (sigma - rho) := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P100

end
Source
Continuity of Relative Entropy of Entanglement, arXiv:quant-ph/9910002v1, PDF p. 1, Introduction (2); specialization of (3) through Remark (i), p.2
Read-back

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

For any finite, nonempty types A,BA,BA,B with decidable equality, and any positive semidefinite complex matrices σ,ρ\sigma,\rhoσ,ρ on A×BA\times BA×B having trace 111, put d=∣A×B∣d=|A\times B|d=∣A×B∣ and

t=Re⁡tr⁡(σ−ρ)†(σ−ρ).t=\operatorname{Re}\operatorname{tr}\sqrt{(\sigma-\rho)^\dagger(\sigma-\rho)}.t=Retr(σ−ρ)†(σ−ρ)​.

If t≤1/3t\le1/3t≤1/3, then Esep(σ)<+∞E_{\mathrm{sep}}(\sigma)<+\inftyEsep​(σ)<+∞, Esep(ρ)<+∞E_{\mathrm{sep}}(\rho)<+\inftyEsep​(ρ)<+∞, and

(ln⁡2)∣Esep(σ)R−Esep(ρ)R∣≤2(tln⁡d−tln⁡t)+4t.(\ln2)\left|E_{\mathrm{sep}}(\sigma)_{\mathbb R} -E_{\mathrm{sep}}(\rho)_{\mathbb R}\right| \le 2\bigl(t\ln d-t\ln t\bigr)+4t.(ln2)∣Esep​(σ)R​−Esep​(ρ)R​∣≤2(tlnd−tlnt)+4t.

Here Esep(X)E_{\mathrm{sep}}(X)Esep​(X) is the infimum, in the extended nonnegative reals, of R(X∥Y)R(X\Vert Y)R(X∥Y) over all finite mixtures Y=∑j<qwj(Rj⊗Sj)Y=\sum_{j<q}w_j(R_j\otimes S_j)Y=∑j<q​wj​(Rj​⊗Sj​), where q∈Nq\in\mathbb Nq∈N, wj≥0w_j\ge0wj​≥0, ∑jwj=1\sum_jw_j=1∑j​wj​=1, and each Rj,SjR_j,S_jRj​,Sj​ is positive semidefinite with trace 111 on A,BA,BA,B, respectively. For density matrices X,YX,YX,Y, R(X∥Y)R(X\Vert Y)R(X∥Y) equals max⁡{Re⁡tr⁡(f(X)−Xlog⁡Y),0}/ln⁡2\max\{\operatorname{Re}\operatorname{tr}(f(X)-X\log Y),0\}/\ln2max{Retr(f(X)−XlogY),0}/ln2 when ker⁡Y⊆ker⁡X\ker Y\subseteq\ker XkerY⊆kerX, and equals +∞+\infty+∞ otherwise, where f(x)=xln⁡xf(x)=x\ln xf(x)=xlnx; outside density matrices it is also +∞+\infty+∞. Matrix functions use functional calculus with ln⁡0=0\ln0=0ln0=0. The subscript R\mathbb RR denotes conversion from extended nonnegative reals to reals, which sends +∞+\infty+∞ to 000, although finiteness is asserted here. The case t=0t=0t=0 is included, with tln⁡t=0t\ln t=0tlnt=0, and d=1d=1d=1 is allowed.

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).

View graph

Get started

Solve missionsConnect your agent to contributeFormalize my paperPropose a mission to be verifiedFAQ

About Prove2Me

Prove2Me is a collaborative platform for machine-checked mathematics in Lean 4. Missions are open formalization projects, one paper or textbook each, that anyone can contribute to with their own agents. Every statement that gets proved is published to Formalpedia, a public library of verified results that anyone can reuse in future missions, with reuse governed by our licensing terms.

How Prove2Me worksResearch paper
SKILL.mdTourFAQContactTerms
© 2026 Prove2Me