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Abstract main result: both low-noise capacities agree with one-shot coherent information to leading orders

Open
QAlgorithms.Papers.Independent.Intakethird100.P50.lowNoise_capacities_leading

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

prove2quantum

All systems are nonzero finite-dimensional complex Hilbert spaces. A channel is complex-linear, completely positive and trace preserving. Its receiver and full complementary channel are obtained from the same isometric dilation. Write Q(N)Q(N)Q(N) and P(N)P(N)P(N) for the unassisted operational quantum and private capacities, allowing arbitrary collective codes over independent uses with vanishing reliability error and environmental leakage. Write Ic(N)=max⁡ρ[S(N(ρ))−S(Nc(ρ))]I_c(N)=\max_\rho[S(N(\rho))-S(N^c(\rho))]Ic​(N)=maxρ​[S(N(ρ))−S(Nc(ρ))], where the maximum includes every density operator. Entropies and logarithms are in bits, with 0log⁡0=00\log 0=00log0=0. The diamond norm has no factor 1/21/21/2.

Fix the input/output space AAA. There are constants C≥0C\ge0C≥0 and 0<δ≤10<\delta\le10<δ≤1, uniform over all channels N:A→AN:A\to AN:A→A and their dilations, such that ∥N−idA∥⋄≤ε\|N-\mathrm{id}_A\|_\diamond\le\varepsilon∥N−idA​∥⋄​≤ε and 0<ε<δ0<\varepsilon<\delta0<ε<δ imply

max⁡{∣Q(N)−Ic(N)∣, ∣P(N)−Ic(N)∣}≤Cε3/2log⁡2(1/ε).\max\{|Q(N)-I_c(N)|,\ |P(N)-I_c(N)|\}\le C\varepsilon^{3/2}\log_2(1/\varepsilon).max{∣Q(N)−Ic​(N)∣, ∣P(N)−Ic​(N)∣}≤Cε3/2log2​(1/ε).

This quantifies the joint leading-order agreement asserted in the abstract using Theorem 12.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P50
Formal statement
noncomputable section

open scoped BigOperators Matrix ComplexConjugate ComplexOrder

open Filter

namespace QAlgorithms.Papers.Independent.Intakethird100.P50

variable {A B E F : Type}

variable [Fintype A] [DecidableEq A] [Fintype B] [DecidableEq B]

variable [Fintype E] [DecidableEq E] [Fintype F] [DecidableEq F]

open QAlgorithms.Papers.Independent.Information100.P67
  (IsIsometry receiverChannel complementaryChannel maximumCoherentInformation
    dilationPower idealJoint)

open QAlgorithms.Papers.Independent.Information100.P80 (log2)

open QAlgorithms.Papers.Independent.Information100.P69 (diamond choi)

open QAlgorithms.Papers.Independent.Support50.P07 (fullTracePrivateCapacity)

open QAlgorithms.Papers.Independent.Frontier18.P17 (clock)

/-- Abstract, PDF p.1, pinned to Theorem 12, PDF p.12: both operational capacities
agree with unrestricted one-shot coherent information up to O(eps^(3/2) log(1/eps)).
Constants are uniform over channels and dilations for each fixed input dimension. -/
theorem lowNoise_capacities_leading [Nonempty A] :
    ∃ C : ℝ, 0 ≤ C ∧ ∃ delta : ℝ, 0 < delta ∧ delta ≤ 1 ∧
      ∀ (E : Type) [Fintype E] [DecidableEq E] [Nonempty E],
      ∀ V : Matrix (A × E) A ℂ, IsIsometry V →
      ∀ eps ∈ Set.Ioo 0 delta,
        diamond (channelDifference (receiverChannel V) id) ≤ eps →
        |dilationQuantumCapacity V - maximumCoherentInformation V| ≤
          C * eps ^ (3 / 2 : ℝ) * (-log2 eps) ∧
        |fullTracePrivateCapacity V - maximumCoherentInformation V| ≤
          C * eps ^ (3 / 2 : ℝ) * (-log2 eps) := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P50

end
Source
Quantum and private capacities of low-noise channels, arXiv:1705.04335v3, PDF p. 1, Abstract main result: both low-noise capacities agree with one-shot coherent information to leading orders
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What the Lean code literally says, in plain math · gpt-6.1-sol

For every finite nonempty type AAA equipped with decidable equality, there exist real constants C≥0C\ge0C≥0 and 0<δ≤10<\delta\le10<δ≤1 such that, for every finite nonempty type EEE equipped with decidable equality, every complex matrix V:A×E←AV:A\times E\leftarrow AV:A×E←A satisfying V†V=IAV^\dagger V=I_AV†V=IA​, and every real 0<ε<δ0<\varepsilon<\delta0<ε<δ, the assumption ∥N−id∥⋄≤ε\|N-\mathrm{id}\|_\diamond\le\varepsilon∥N−id∥⋄​≤ε implies

∣Q(N)−Imax⁡(V)∣≤Cε3/2(−log⁡2ε),∣P(V)−Imax⁡(V)∣≤Cε3/2(−log⁡2ε).|Q(N)-I_{\max}(V)|\le C\varepsilon^{3/2}(-\log_2\varepsilon), \qquad |P(V)-I_{\max}(V)|\le C\varepsilon^{3/2}(-\log_2\varepsilon).∣Q(N)−Imax​(V)∣≤Cε3/2(−log2​ε),∣P(V)−Imax​(V)∣≤Cε3/2(−log2​ε).

Here N(ρ)=Tr⁡E(VρV†)N(\rho)=\operatorname{Tr}_E(V\rho V^\dagger)N(ρ)=TrE​(VρV†), Nc(ρ)=Tr⁡A(VρV†)N^c(\rho)=\operatorname{Tr}_A(V\rho V^\dagger)Nc(ρ)=TrA​(VρV†), and

Imax⁡(V)=sup⁡ρ≥0, Tr⁡ρ=1[H(N(ρ))−H(Nc(ρ))],H(ρ)=−Re⁡Tr⁡[(x↦xlog⁡2x)(ρ)],I_{\max}(V)=\sup_{\rho\ge0,\ \operatorname{Tr}\rho=1} \bigl[H(N(\rho))-H(N^c(\rho))\bigr], \qquad H(\rho)=-\operatorname{Re}\operatorname{Tr}\bigl[(x\mapsto x\log_2x)(\rho)\bigr],Imax​(V)=ρ≥0, Trρ=1sup​[H(N(ρ))−H(Nc(ρ))],H(ρ)=−ReTr[(x↦xlog2​x)(ρ)],

using spectral functional calculus. For a map Φ\PhiΦ with input index type III, ∥Φ∥⋄\|\Phi\|_\diamond∥Φ∥⋄​ denotes the real supremum of ∥(idI⊗Φ)(X)∥1\|(\mathrm{id}_I\otimes\Phi)(X)\|_1∥(idI​⊗Φ)(X)∥1​ over all complex matrices XXX on I×II\times II×I with ∥X∥1≤1\|X\|_1\le1∥X∥1​≤1, where ∥X∥1=Re⁡Tr⁡X†X\|X\|_1=\operatorname{Re}\operatorname{Tr}\sqrt{X^\dagger X}∥X∥1​=ReTrX†X​, and amplification applies Φ\PhiΦ separately to each reference matrix block. A channel means a map ρ↦∑jKjρKj†\rho\mapsto\sum_jK_j\rho K_j^\daggerρ↦∑j​Kj​ρKj†​ for a finite family with ∑jKj†Kj=I\sum_jK_j^\dagger K_j=I∑j​Kj†​Kj​=I. The number Q(N)Q(N)Q(N) is the real supremum of nonnegative rates rrr admitting, for every natural block length nnn, a positive message dimension MnM_nMn​, a channel encoding that dimension into AnA^nAn, and a channel decoding AnA^nAn back into it, such that the overlap of (id⊗(Decn∘N⊗n∘Encn))(∣ϕMn⟩⟨ϕMn∣)(\mathrm{id}\otimes(\mathrm{Dec}_n\circ N^{\otimes n}\circ\mathrm{Enc}_n))(|\phi_{M_n}\rangle\langle\phi_{M_n}|)(id⊗(Decn​∘N⊗n∘Encn​))(∣ϕMn​​⟩⟨ϕMn​​∣) with ∣ϕMn⟩⟨ϕMn∣|\phi_{M_n}\rangle\langle\phi_{M_n}|∣ϕMn​​⟩⟨ϕMn​​∣ tends to one, and, for every s>0s>0s>0, eventually r−s≤log⁡2Mn/nr-s\le\log_2M_n/nr−s≤log2​Mn​/n; here ϕM=M−1/2∑j=0M−1∣j,j⟩\phi_M=M^{-1/2}\sum_{j=0}^{M-1}|j,j\rangleϕM​=M−1/2∑j=0M−1​∣j,j⟩. The number P(V)P(V)P(V) is the real supremum of nonnegative rates rrr such that, for every η>0\eta>0η>0 and s>0s>0s>0, some positive n0n_0n0​ ensures that every n≥n0n\ge n_0n≥n0​ admits a positive number MMM of density matrices ρm\rho_mρm​ on AnA^nAn and positive semidefinite measurement matrices FjF_jFj​ on AnA^nAn summing to the identity, with r−s≤log⁡2M/nr-s\le\log_2M/nr−s≤log2​M/n and ∥Ω−JM(σ)∥1<η\|\Omega-J_M(\sigma)\|_1<\eta∥Ω−JM​(σ)∥1​<η, where, writing Vn((bi),(ei)),(ai)=∏iV(bi,ei),aiV_n((b_i),(e_i)),(a_i)=\prod_iV_{(b_i,e_i),a_i}Vn​((bi​),(ei​)),(ai​)=∏i​V(bi​,ei​),ai​​,

Ω(m,j,e),(m′,j′,e′)=1m=m′1j=j′M∑b,c(Fj)c,b(VnρmVn†)(b,e),(c,e′),σe,e′=∑m,jΩ(m,j,e),(m,j,e′),JM(σ)(m,j,e),(m′,j′,e′)=1m=j1m′=j′1m=m′M σe,e′.\Omega_{(m,j,e),(m',j',e')} =\frac{\mathbf1_{m=m'}\mathbf1_{j=j'}}M \sum_{b,c}(F_j)_{c,b}(V_n\rho_mV_n^\dagger)_{(b,e),(c,e')}, \quad \sigma_{e,e'}=\sum_{m,j}\Omega_{(m,j,e),(m,j,e')}, \quad J_M(\sigma)_{(m,j,e),(m',j',e')} =\frac{\mathbf1_{m=j}\mathbf1_{m'=j'}\mathbf1_{m=m'}}M\,\sigma_{e,e'}.Ω(m,j,e),(m′,j′,e′)​=M1m=m′​1j=j′​​b,c∑​(Fj​)c,b​(Vn​ρm​Vn†​)(b,e),(c,e′)​,σe,e′​=m,j∑​Ω(m,j,e),(m,j,e′)​,JM​(σ)(m,j,e),(m′,j′,e′)​=M1m=j​1m′=j′​1m=m′​​σe,e′​.

Tensor powers act on tensor products of matrix units by the corresponding tensor products of their images. The constants C,δC,\deltaC,δ are chosen before E,V,εE,V,\varepsilonE,V,ε, so the same constants apply to all those choices; ε=0\varepsilon=0ε=0 and ε=δ\varepsilon=\deltaε=δ are excluded from the asserted bounds.

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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