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Eq.(44): equivalence of AEP and operational cost identity

Open
QAlgorithms.Papers.Independent.Intakethird100.P65.aep_iff_operational

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.

For a CPTP channel N\mathcal NN, the channel AEP and the equality of entanglement-assisted capacity with NS simulation cost are equivalent:

[lim⁡ε↓0lim⁡n→∞n−1Imax⁡ε(N⊗n)=I(N)]⟺QE(N)=SNS(N).\left[\lim_{\varepsilon\downarrow0}\lim_{n\to\infty}n^{-1}I_{\max}^{\varepsilon}(\mathcal N^{\otimes n})=I(\mathcal N)\right]\quad\Longleftrightarrow\quad Q_E(\mathcal N)=S_{\rm NS}(\mathcal N).[ε↓0lim​n→∞lim​n−1Imaxε​(N⊗n)=I(N)]⟺QE​(N)=SNS​(N).

The limit assertion includes existence in the printed order. Both operational quantities are defined from actual codes; the cited background capacity formula is not made a hypothesis.

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]

/-- Eq.(44), PDF p.10: two-way equivalence, without assuming the cited
entanglement-assisted vanishingErrorQuantumCapacity identity as a new hypothesis. -/
theorem aep_iff_operational (N : Superop A B) (hN : IsChannel N) :
    IteratedLimit (fun e n => smoothChannelInfo (channelPower N n) e / n) (channelMutualInfo N) ↔
      vanishingErrorQuantumCapacity .ent N = vanishingErrorSimulationCost .ns N := 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. 10, Eq.(44): equivalence of AEP and operational cost identity
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What the Lean code literally says, in plain math · gpt-6.1-sol

For finite index types A,BA,BA,B, with decidable equality, let N:A→BN:A\to BN:A→B be a channel, meaning a finite Kraus sum with ∑iKi†Ki=IA\sum_iK_i^\dagger K_i=I_A∑i​Ki†​Ki​=IA​. The declaration asserts an equivalence between the iterated-limit statement and the operational equality defined here. Put S(X)=−Re⁡tr⁡cfc⁡(x↦xlog⁡2x)(X)S(X)=-\operatorname{Re}\operatorname{tr}\operatorname{cfc}(x\mapsto x\log_2x)(X)S(X)=−Retrcfc(x↦xlog2​x)(X), vρ(a,a′)=(ρ)a,a′v_\rho(a,a')=(\sqrt\rho)_{a,a'}vρ​(a,a′)=(ρ​)a,a′​, Rρ=(idA⊗N)(∣vρ⟩⟨vρ∣)R_\rho=(\mathrm{id}_A\otimes N)(|v_\rho\rangle\langle v_\rho|)Rρ​=(idA​⊗N)(∣vρ​⟩⟨vρ​∣), and

C(N)=sup⁡ρ density[S(Tr⁡BRρ)+S(Tr⁡ARρ)−S(Rρ)].C(N)=\sup_{\rho\text{ density}} \bigl[S(\operatorname{Tr}_B R_\rho)+S(\operatorname{Tr}_A R_\rho)-S(R_\rho)\bigr].C(N)=ρ densitysup​[S(TrB​Rρ​)+S(TrA​Rρ​)−S(Rρ​)].

Density matrices are positive semidefinite with trace 111. Define Dmax⁡(R∥T)=inf⁡{λ∈R:2λT−R≥0}D_{\max}(R\|T)=\inf\{\lambda\in\mathbb R:2^\lambda T-R\ge0\}Dmax​(R∥T)=inf{λ∈R:2λT−R≥0} in the extended reals, Imax⁡(R)=inf⁡σ densityDmax⁡(R∥(Tr⁡BR)⊗σ)I_{\max}(R)=\inf_{\sigma\text{ density}}D_{\max}(R\|(\operatorname{Tr}_B R)\otimes\sigma)Imax​(R)=infσ density​Dmax​(R∥(TrB​R)⊗σ), and

Ie(T)=inf⁡M channel12∥M−T∥⋄≤e(Imax⁡((id⊗M)(Φ)))toReal,I_e(T)=\inf_{\substack{M\text{ channel}\\\frac12\|M-T\|_\diamond\le e}} \left(I_{\max}((\mathrm{id}\otimes M)(\Phi))\right)_{\mathrm{toReal}},Ie​(T)=M channel21​∥M−T∥⋄​≤e​inf​(Imax​((id⊗M)(Φ)))toReal​,

where Φ\PhiΦ is the normalized maximally entangled matrix on the input and an identical reference, with entry the inverse input dimension when both index pairs have equal coordinates and 000 otherwise; either extended-real infinity converts to 000. The diamond norm uses all matrices of trace norm at most 111 on an input-sized reference tensor the input, with ∥X∥1=Re⁡tr⁡X†X\|X\|_1=\operatorname{Re}\operatorname{tr}\sqrt{X^\dagger X}∥X∥1​=RetrX†X​. The iterated-limit statement is that there exists L:R→RL:\mathbb R\to\mathbb RL:R→R such that, for every 0<e<10<e<10<e<1,

lim⁡j→∞Ie(N⊗(j+1))j+1=L(e),lim⁡e↓0L(e)=C(N).\lim_{j\to\infty}\frac{I_e(N^{\otimes(j+1)})}{j+1}=L(e), \qquad \lim_{e\downarrow0}L(e)=C(N).j→∞lim​j+1Ie​(N⊗(j+1))​=L(e),e↓0lim​L(e)=C(N).

To define the operational side, for F:U→VF:U\to VF:U→V and G:X→YG:X\to YG:X→Y, let Eo(F,G)E_o(F,G)Eo​(F,G) be the infimum of 12∥WF(P)−G∥⋄\tfrac12\|\mathcal W_F(P)-G\|_\diamond21​∥WF​(P)−G∥⋄​, where

WF(P)(Z)y,y′=∑a,c,u,vF(∣a⟩⟨c∣)u,vP(Z⊗∣u⟩⟨v∣)(a,y),(c,y′).\mathcal W_F(P)(Z)_{y,y'} =\sum_{a,c,u,v} F(|a\rangle\langle c|)_{u,v} P(Z\otimes|u\rangle\langle v|)_{(a,y),(c,y')}.WF​(P)(Z)y,y′​=a,c,u,v∑​F(∣a⟩⟨c∣)u,v​P(Z⊗∣u⟩⟨v∣)(a,y),(c,y′)​.

Nonsignalling assistance permits channels P:X×V→U×YP:X\times V\to U\times YP:X×V→U×Y whose two output marginals, on every matrix, are channel functions of their respective input marginals. Entanglement assistance permits precisely maps obtained by adjoining a density matrix on two finite positive-dimensional ancillary systems and applying a channel locally to each input together with its ancillary share. Let Qent(N)Q_{\mathrm{ent}}(N)Qent​(N) be the extended-real supremum of nonnegative real rrr such that, for every e>0e>0e>0 and slack s>0s>0s>0, eventually every positive nnn admits a positive integer mmm with Eent(N⊗n,idm)≤eE_{\mathrm{ent}}(N^{\otimes n},\mathrm{id}_m)\le eEent​(N⊗n,idm​)≤e and (r−s)n≤log⁡2m(r-s)n\le\log_2m(r−s)n≤log2​m. Let Cns(N)C_{\mathrm{ns}}(N)Cns​(N) be the extended-real infimum of nonnegative real rrr with the corresponding conditions Ens(idm,N⊗n)≤eE_{\mathrm{ns}}(\mathrm{id}_m,N^{\otimes n})\le eEns​(idm​,N⊗n)≤e and log⁡2m≤(r+s)n\log_2m\le(r+s)nlog2​m≤(r+s)n. Tensor powers act by (N⊗n(X))y,z=∑x,x′Xx,x′∏iN(∣xi⟩⟨xi′∣)yi,zi(N^{\otimes n}(X))_{y,z}=\sum_{x,x'}X_{x,x'}\prod_iN(|x_i\rangle\langle x'_i|)_{y_i,z_i}(N⊗n(X))y,z​=∑x,x′​Xx,x′​∏i​N(∣xi​⟩⟨xi′​∣)yi​,zi​​. The asserted equivalence is that the stated iterated limit holds if and only if Qent(N)=Cns(N)Q_{\mathrm{ent}}(N)=C_{\mathrm{ns}}(N)Qent​(N)=Cns​(N); the operational error conditions concern infima and do not assert realizing assistance maps.

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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