Loss-tolerant steering inequality with a public detection flag
OpenQAlgorithms.Papers.Independent.Intakethird100.P94.lossTolerantSteering_S1Let Bob's two binary qubit projective measurements have orthonormal bases and , with . Define . Suppose the observed probabilities admit a single normalized local-hidden-state ensemble for all setting pairs: Alice's response can depend on and the hidden variable, and Bob measures the associated normalized qubit state in basis . Here means Alice detects, and her outcome at is predetermined.
Assume Alice's detection probability is the same, , at both settings. Let use all outputs, including no-click assignments. Write when , where ; set at . The binary entropy is in bits. Then
At zero efficiency, . Violation excludes a local-hidden-state description. This is the steering inequality corresponding algebraically to the nonpositive right-hand side of Eq. (8); no operational key-security implication is asserted by this item.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P94
noncomputable section
namespace QAlgorithms.Papers.Independent.Intakethird100.P94
open MeasureTheory
open scoped ComplexConjugate
/-- arXiv:1109.1435v3, PDF pp.3,6, (S1): loss-tolerant steering inequality.
The printed eta is eta_A from (S6)--(S9). The weighted form includes eta_A=0;
the additional zero branch spells out q<=1 without an undefined postselected error. -/
theorem lossTolerantSteering_S1 {Lambda : Type*} [MeasurableSpace Lambda]
(U : BobBases) (L : HiddenModel Lambda U)
(hCommonEfficiency : detectionProbability L false = detectionProbability L true) :
projectiveOverlapIncompatibility U ≤ weightedDetectedEntropy L false +
QAlgorithms.binEntropy2 (fullError L true) + (1 - detectionProbability L false) ∧
(detectionProbability L false = 0 → projectiveOverlapIncompatibility U ≤ 1) := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P94
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
Let , representing false and true. For every measurable space , two unitary complex matrices indexed by , and a model , assume consists of a probability measure ; positive semidefinite trace-one matrices with measurable entries; nonnegative responses , measurable in , satisfying for every ; and bits such that whenever . Set , , and , with all products required to be integrable. Define
where , with , and put . Under the additional hypothesis , both conclusions hold:
The equal-efficiency hypothesis permits both efficiencies to be zero.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.