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§4 unnumbered main result: quadratic degradability for generalized qudit Pauli channels

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QAlgorithms.Papers.Independent.Intakethird100.P50.quditPauli_quadraticDegradability

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

prove2quantum

Fix an integer dimension d≥1d\ge1d≥1. In the standard basis define X∣j⟩=∣j+1(modd)⟩X|j\rangle=|j+1\pmod d\rangleX∣j⟩=∣j+1(modd)⟩ and Z∣j⟩=e2πij/d∣j⟩Z|j\rangle=e^{2\pi i j/d}|j\rangleZ∣j⟩=e2πij/d∣j⟩. For any probability distribution wkℓw_{k\ell}wkℓ​ on 0≤k,ℓ<d0\le k,\ell<d0≤k,ℓ<d, let Nw(ρ)=∑k,ℓwkℓXkZℓρ(XkZℓ)†N_w(\rho)=\sum_{k,\ell}w_{k\ell}X^kZ^\ell\rho(X^kZ^\ell)^\daggerNw​(ρ)=∑k,ℓ​wkℓ​XkZℓρ(XkZℓ)† and use its canonical Kraus-label dilation. There exist Cd≥0C_d\ge0Cd​≥0 and 0<δd≤20<\delta_d\le20<δd​≤2 such that, uniformly over www,

∥Nw−idd∥⋄≤ε≤δd⟹dg⁡(Nw)≤Cdε2(ε≥0).\|N_w-\mathrm{id}_d\|_\diamond\le\varepsilon\le\delta_d \quad\Longrightarrow\quad \operatorname{dg}(N_w)\le C_d\varepsilon^2\qquad(\varepsilon\ge0).∥Nw​−idd​∥⋄​≤ε≤δd​⟹dg(Nw​)≤Cd​ε2(ε≥0).

The degradability parameter optimizes the full diamond error over all CPTP degrading maps. This is the stated class-level improvement for generalized Pauli noise; no polynomial-family hypothesis or dimension-independent constant is imposed.

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)

/-- §4 unnumbered main result, PDF pp.12,19: dimension-dependent but channel-uniform
quadratic bound for ALL generalized Pauli probability distributions near identity. -/
theorem quditPauli_quadraticDegradability (d : ℕ) (hd : 0 < d) :
    ∃ C : ℝ, 0 ≤ C ∧ ∃ delta : ℝ, 0 < delta ∧ delta ≤ 2 ∧
      ∀ w : (Fin d × Fin d) → ℝ, Probabilities w →
      ∀ eps ∈ Set.Icc 0 delta,
        diamond (channelDifference (receiverChannel (quditPauliDilation d w)) id) ≤ eps →
        degradability (quditPauliDilation d w) ≤ C * eps ^ 2 := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P50

end
Source
Quantum and private capacities of low-noise channels, arXiv:1705.04335v3, PDF p. 12, §4 unnumbered main result: quadratic degradability for generalized qudit Pauli channels
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What the Lean code literally says, in plain math · gpt-6.1-sol

For every positive integer ddd, there are real constants C≥0C\ge0C≥0 and 0<δ≤20<\delta\le20<δ≤2 with the following property. Index the standard basis of Cd\mathbb C^dCd by 0,…,d−10,\ldots,d-10,…,d−1, let Sij=1S_{ij}=1Sij​=1 when i=j+1(modd)i=j+1\pmod di=j+1(modd) and 000 otherwise, and let Zjj=e2πij/dZ_{jj}=e^{2\pi\mathrm{i}j/d}Zjj​=e2πij/d. For every collection of real weights wrs≥0w_{rs}\ge0wrs​≥0 with ∑r,swrs=1\sum_{r,s}w_{rs}=1∑r,s​wrs​=1, define

Vb,(r,s);a=wrs (SrZs)ba,NV(X)=Tr⁡E(VXV†),NVc(X)=Tr⁡B(VXV†).V_{b,(r,s);a}=\sqrt{w_{rs}}\,(S^rZ^s)_{ba},\qquad N_V(X)=\operatorname{Tr}_{E}(VXV^\dagger),\qquad N_V^c(X)=\operatorname{Tr}_{B}(VXV^\dagger).Vb,(r,s);a​=wrs​​(SrZs)ba​,NV​(X)=TrE​(VXV†),NVc​(X)=TrB​(VXV†).

Here both BBB and the input have dimension ddd, and EEE is indexed by the d2d^2d2 pairs (r,s)(r,s)(r,s). For a matrix map TTT with input index set HHH, write

∥T∥⋄=sup⁡∥X∥1≤1∥(id⁡H⊗T)(X)∥1,\|T\|_\diamond=\sup_{\|X\|_1\le1}\|(\operatorname{id}_{H}\otimes T)(X)\|_1,∥T∥⋄​=∥X∥1​≤1sup​∥(idH​⊗T)(X)∥1​,

where XXX ranges over all complex matrices on H×HH\times HH×H, without positivity or Hermiticity restrictions, and ∥X∥1=Re⁡Tr⁡X†X\|X\|_1=\operatorname{Re}\operatorname{Tr}\sqrt{X^\dagger X}∥X∥1​=ReTrX†X​. Define

η(V)=inf⁡{η∈[0,2]:∃D:B→E a channel, ∥NVc−D∘NV∥⋄≤η},\eta(V)=\inf\{\eta\in[0,2]:\exists D:B\to E\text{ a channel},\ \|N_V^c-D\circ N_V\|_\diamond\le\eta\},η(V)=inf{η∈[0,2]:∃D:B→E a channel, ∥NVc​−D∘NV​∥⋄​≤η},

where a channel means a map X↦∑jKjXKj†X\mapsto\sum_jK_jXK_j^\daggerX↦∑j​Kj​XKj†​ with finitely many Kraus matrices satisfying ∑jKj†Kj=I\sum_jK_j^\dagger K_j=I∑j​Kj†​Kj​=I. Then every ε∈[0,δ]\varepsilon\in[0,\delta]ε∈[0,δ] satisfies

∥NV−id⁡∥⋄≤ε⟹η(V)≤Cε2.\|N_V-\operatorname{id}\|_\diamond\le\varepsilon \quad\Longrightarrow\quad \eta(V)\le C\varepsilon^2.∥NV​−id∥⋄​≤ε⟹η(V)≤Cε2.

The constants are chosen before the weights and ε\varepsilonε; the assertion includes d=1d=1d=1 and ε=0\varepsilon=0ε=0.

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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