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Diamond distance dominates unassisted channel trace distance

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QAlgorithms.Papers.Independent.Intakethird100.P13.diamond_dominatesTrace

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

prove2quantum

Let E1,E2E_1,E_2E1​,E2​ be trace-preserving completely positive maps from a qubit to a qubit. Define the unassisted distance by the supremum of ∥E1(ρ)−E2(ρ)∥1\|E_1(\rho)-E_2(\rho)\|_1∥E1​(ρ)−E2​(ρ)∥1​ over normalized qubit density matrices. Define the diamond distance by the same supremum after applying id2⊗Ei\mathrm{id}_2\otimes E_iid2​⊗Ei​ to normalized two-qubit density matrices. Then

∥E1−E2∥⋄≥∥E1−E2∥1.\|E_1-E_2\|_\diamond\ge\|E_1-E_2\|_1.∥E1​−E2​∥⋄​≥∥E1​−E2​∥1​.

Both norms are full trace norms, without a factor of one half. This compares assisted and unassisted channel distinguishability.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P13
Formal statement
noncomputable section

open scoped BigOperators Matrix ComplexConjugate ComplexOrder

namespace QAlgorithms.Papers.Independent.Intakethird100.P13

attribute [local instance] Classical.propDecidable Classical.decEq

/-- arXiv:1706.05384v2 PDF pp.9,14, Lemma 25: two actual qubit channels,
normalized density inputs and the full trace norm on both sides. -/
theorem diamond_dominatesTrace (E F : QubitMap) (hE : IsChannel E) (hF : IsChannel F) :
    channelTraceDistance E F ≤ channelDiamondDistance E F := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P13

end
Source
Simulation of non-Pauli Channels, arXiv:1706.05384v2, PDF p. 14, Lemma 25
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What the Lean code literally says, in plain math · gpt-6.1-sol

For every two maps E,F:M2(C)→M2(C)E,F:M_2(\mathbb C)\to M_2(\mathbb C)E,F:M2​(C)→M2​(C), assume each has a finite Kraus representation: for each G∈{E,F}G\in\{E,F\}G∈{E,F}, there are a natural number mGm_GmG​ and matrices KG,0,…,KG,mG−1∈M2(C)K_{G,0},\ldots,K_{G,m_G-1}\in M_2(\mathbb C)KG,0​,…,KG,mG​−1​∈M2​(C) such that ∑jKG,j∗KG,j=I2\sum_jK_{G,j}^{*}K_{G,j}=I_2∑j​KG,j∗​KG,j​=I2​ and G(X)=∑jKG,jXKG,j∗G(X)=\sum_jK_{G,j}XK_{G,j}^{*}G(X)=∑j​KG,j​XKG,j∗​ for every matrix XXX. Then

sup⁡ρ∈M2(C)ρ⪰0, tr⁡ρ=1tr⁡(E(ρ)−F(ρ))∗(E(ρ)−F(ρ)) ≤ sup⁡ρ∈M4(C)ρ⪰0, tr⁡ρ=1tr⁡Dρ∗Dρ,Dρ=(id⁡2⊗E)(ρ)−(id⁡2⊗F)(ρ).\sup_{\substack{\rho\in M_2(\mathbb C)\\ \rho\succeq0,\ \operatorname{tr}\rho=1}} \operatorname{tr}\sqrt{(E(\rho)-F(\rho))^{*}(E(\rho)-F(\rho))} \ \le\ \sup_{\substack{\rho\in M_4(\mathbb C)\\ \rho\succeq0,\ \operatorname{tr}\rho=1}} \operatorname{tr}\sqrt{D_\rho^{*}D_\rho}, \qquad D_\rho=(\operatorname{id}_2\otimes E)(\rho)-(\operatorname{id}_2\otimes F)(\rho).ρ∈M2​(C)ρ⪰0, trρ=1​sup​tr(E(ρ)−F(ρ))∗(E(ρ)−F(ρ))​ ≤ ρ∈M4​(C)ρ⪰0, trρ=1​sup​trDρ∗​Dρ​​,Dρ​=(id2​⊗E)(ρ)−(id2​⊗F)(ρ).

Here id⁡2⊗G\operatorname{id}_2\otimes Gid2​⊗G applies GGG to each 2×22\times22×2 block of a matrix indexed by a pair of Boolean indices, leaving the first index as the block index; the square roots are the positive semidefinite matrix square roots. Neither supremum includes a factor of 1/21/21/2.

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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