Proposition 2 (Upper Bound in Secret Key Capacity)
OpenQAlgorithms.Papers.Independent.Intakethird100.P76.secret_key_upper_boundLet be finite nonempty alphabets and let be a normalized discrete memoryless broadcast channel. All entropies and information quantities are in bits. For an input law , information quantities below use exactly the induced law .
Alice draws a uniform finite message , uses a possibly stochastic encoder to excite channel uses, and observes . A public forward message is visible to Bob and Eve. Their keys are and . All design maps are fixed publicly before observations. Message and public alphabets follow the fixed-rate ceiling convention; the key alphabet at rate has exactly labels. A weakly achievable rate has one sequence of such codes with disagreement tending to zero, , and .
Let be bounded and nonnegative, and let be admissible, meaning that a normalized input law of expected cost at most exists. Every operational code obeys almost surely; each single-letter optimization obeys . Excitation and public rates are unrestricted.
The cost-feasible single-letter maximum is attained, and
This upper bound requires no auxiliary variables.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P76
namespace QAlgorithms.Papers.Independent.Intakethird100.P76
noncomputable section
open scoped BigOperators
open Filter Set
attribute [local instance] Classical.propDecidable
-- Exact pinned primitives, inspected at their definitions before reuse.
open QAlgorithms.Papers.Independent.Information100.P56 (IsLaw)
open QAlgorithms.Papers.Independent.Frontier30.P12 (IsStochastic)
variable {S X Y Z : Type} [Fintype S] [Fintype X] [Fintype Y] [Fintype Z]
[Nonempty S] [Nonempty X] [Nonempty Y] [Nonempty Z]
variable {n u w : ℕ}
/-- PDF p.5, Proposition 2, (11): an attained cost-feasible upper bound. -/
theorem secret_key_upper_bound (W : DMBC S X Y Z) (cost : S → ℝ) (Gamma : ℝ)
(hcost : AdmissibleCost cost Gamma) :
∃ p : S → ℝ, CostFeasibleInput cost Gamma p ∧
IsGreatest {v | ∃ q, CostFeasibleInput cost Gamma q ∧ v = upperObjective W q}
(upperObjective W p) ∧
senderExcitedCapacity W cost (some Gamma) ≤ (upperObjective W p : EReal) := by sorry
end
end QAlgorithms.Papers.Independent.Intakethird100.P76
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What the Lean code literally says, in plain math · gpt-6.1-sol
For finite types , let give their nonemptiness and conditional probability laws . Suppose and satisfy , for every for some real , and the existence of a probability law whose expected cost is at most . There exists a probability law on with such that
belongs to, and is at least every member of, the set of over all probability laws satisfying , and the sender-excited capacity is at most , embedded in the extended real numbers. Here , with Shannon entropy in bits. The capacity being bounded is the extended-real supremum of nonnegative real rates admitting a sequence of codes at all positive block lengths : each code has positive message, public-message, and key counts , normalized nonnegative encoders , a public map , an Alice key map , and a Bob key map ; for fixed nonnegative constants , their counts satisfy , , and ; every encoder-supported word has ; and under , key disagreement tends to zero, tends to zero, and for every , eventually .
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.