Binary conditional entropy is bounded by the entropy of the error
ProvedQAlgorithms.Papers.Independent.Intakethird100.P94.binaryConditionalEntropy_S7prove2quantum
Let and be binary random variables with any normalized joint probability law. Let , and let , with . Then
The entropy is measured in bits, and a zero-probability conditioning event has weight zero. This bound converts observed binary disagreement probabilities into conditional-entropy upper bounds for the steering inequalities.
Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P94
Formal statement
noncomputable section
namespace QAlgorithms.Papers.Independent.Intakethird100.P94
open MeasureTheory
open scoped ComplexConjugate
/-- arXiv:1109.1435v3, PDF p.6, (S7): for every normalized joint binary law,
H(B|A) <= h(Pr[A != B]), including zero-mass conditioning events. -/
theorem binaryConditionalEntropy_S7 (P : Bool → Bool → ℝ)
(hNonneg : ∀ a b, 0 ≤ P a b)
(hTotal : ∑ a : Bool, ∑ b : Bool, P a b = 1) :
conditionalEntropyBits P ≤ QAlgorithms.binEntropy2 (binaryError P) := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P94
end
Source
One-sided Device-Independent Quantum Key Distribution: Security, feasibility, and the connection with steering, arXiv:1109.1435v3, PDF p. 6, Eq. (S7)
Read-back
What the Lean code literally says, in plain math · gpt-6.1-sol
For every real-valued array , indexed by , assume that for every pair and that . Put , let , and define , with . Then
The assumptions allow zero rows and error probabilities or ; zero rows contribute zero.
Human review
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