Nonuniform private inputs have lower entropy cost
ProvedQAlgorithms.Papers.Independent.Intakethird100.P47.nonuniform_input_entropyLet and be independent private binary settings, each with probability of input zero. Let , with , and let be Shannon entropy in bits. Then
In particular lies strictly between zero and one and has . This is the asymptotic Shannon randomness cost of the independent input source, not a finite-seed length or a security statement for correlated adversarial randomness.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P47
namespace QAlgorithms.Papers.Independent.Intakethird100.P47
open scoped ComplexConjugate
open scoped BigOperators
/-- IV B / VII A, PDF pp.6,8–9: nonuniform independent private settings cost less
Shannon entropy than two fair bits; the ideal chosen bias is included explicitly.
This is a population entropy statement, not an exact finite random-seed length. -/
theorem nonuniform_input_entropy :
(∀ r : ℝ, 0 ≤ r → r ≤ 1 →
pairEntropy (binarySettingLaw r r) = 2 * binEntropy2 r ∧
(r ≠ 1 / 2 → pairEntropy (binarySettingLaw r r) < 2)) ∧
(0 < (Real.sqrt 5 - 1) / 2 ∧ (Real.sqrt 5 - 1) / 2 < 1) ∧
pairEntropy (binarySettingLaw ((Real.sqrt 5 - 1) / 2) ((Real.sqrt 5 - 1) / 2)) < 2 := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P47
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What the Lean code literally says, in plain math · gpt-6.1-sol
For , define , , and the pair law on . For a pair law , write , where is the natural logarithm and a zero entry contributes zero. Write , with the same zero-term convention. The declaration asserts the conjunction of these claims: for every real with , including and ,
the number satisfies ; and .
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.