Proposition 3: nonnegativity and vanishing on local boxes
OpenQAlgorithms.Papers.Independent.Intakethird100.P22.nonlocality_nonnegative_localAll input and output alphabets are finite and nonempty. Let be a normalized nonnegative bipartite non-signaling device, and let be its complete extension, with possibly input-dependent pure-member labels. All Shannon information quantities are in bits, with . Write for the maximum over honest settings of the infimum, over Eve's general measurements, of intrinsic information; intrinsic information itself minimizes classical conditional mutual information over arbitrary stochastic channels on Eve.
Then
Vanishing on local devices is one-way and asserts no faithfulness converse.
Eve retains the dice result and extension output . Squashed CMI conditions on ; intrinsic-information channels may depend on both. This is the operational convention of XI.B.3 used by the proof, rather than discarding the dice result in display (7).
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P22
noncomputable section
open scoped BigOperators
namespace QAlgorithms.Papers.Independent.Intakethird100.P22
attribute [local instance] Classical.propDecidable Classical.decEq
open QAlgorithms.Papers.Independent.Advance50.P47
(Box IsCorrelation IsNoSignaling HasLocalModel correlationMixture prBox)
open QAlgorithms.Papers.Independent.Information100.P56 (IsLaw)
open QAlgorithms.Papers.Independent.Information100.P27 (shannon)
open QAlgorithms.Papers.Independent.Gap48.P25 (classicalMI)
section Devices
variable {X Y A B : Type} [Fintype X] [Fintype Y] [Fintype A] [Fintype B]
[Nonempty X] [Nonempty Y] [Nonempty A] [Nonempty B]
/-- PDF p.45, Proposition 3, (219): nonnegative and zero on local boxes;
no converse, optimizer, or favorable extension is assumed. Source convention: Eve keeps the dice
result (p.22, XI.B.3); CE members may depend on (z,e) -/
theorem nonlocality_nonnegative_local {p : Box X Y A B} (c : CompleteExtension p) :
0 ≤ squashedNonlocality c ∧ (HasLocalModel p → squashedNonlocality c = 0) := by sorry
end Devices
end QAlgorithms.Papers.Independent.Intakethird100.P22
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
For finite nonempty alphabets , any box , and any complete extension of , the assertion is , and, if has a local model, then . A complete extension consists of positive finite index sets , common valid boxes , and probability weights reconstructing for every ; valid means nonnegative, normalized at every input, and no-signaling in both directions. Each positive-weight support consists of vertices and has no proper subset supporting any finite valid decomposition of , and each such minimal vertex decomposition is represented by exactly one , with equality of total mass at every box. For probability weights , define ; then
where . Having a local model means that is nonnegative and normalized and that there exist a type , a probability mass function on , and probability response vectors , such that ; is not required to be finite.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.