Diamond distance dominates unassisted channel trace distance
OpenQAlgorithms.Papers.Independent.Intakethird100.P13.diamond_dominatesTraceLet be trace-preserving completely positive maps from a qubit to a qubit. Define the unassisted distance by the supremum of over normalized qubit density matrices. Define the diamond distance by the same supremum after applying to normalized two-qubit density matrices. Then
Both norms are full trace norms, without a factor of one half. This compares assisted and unassisted channel distinguishability.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P13
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
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What the Lean code literally says, in plain math · gpt-6.1-sol
For every two maps , assume each has a finite Kraus representation: for each , there are a natural number and matrices such that and for every matrix . Then
Here applies to each 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 .
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.