Positive signal surplus yields enough matching bases with probability tending to one
ProvedQAlgorithms.Papers.Independent.Intakenext100.P17.sifting_surplusFix an overhead and let . Assume all ideal signals are received. Alice and Bob independently choose uniform strings specifying their preparation and measurement bases. Let . Then
Here the probability is the explicit uniform average over all ordered pairs , and a shortage of matches is an abort. This gives the sifting surplus in Protocol 3, step 6; it concerns matching bases, not the later error test.
Formalization Note The integer ceiling implements the source's signal count and agrees with it whenever is integral. The overhead is called to distinguish it from an error threshold.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakenext100_P17
namespace QAlgorithms.Papers.Independent.Intakenext100.P17
noncomputable section
open scoped ComplexConjugate
open Classical
open QAlgorithms.DopedTomography (bellVector)
open QAlgorithms.Papers.Independent.Support50.P04 (BinaryWord)
/-- PDF p.4, Protocol 3 step 6: for each fixed positive surplus gamma, the
probability that at least 2n matching-basis positions remain tends to one.
Only ideal receipt and independent uniform bases enter this combinatorial
statement. It does not assert acceptance of the subsequent error test. -/
theorem sifting_surplus (gamma : ℝ) (hgamma : 0 < gamma) :
Filter.Tendsto (fun n : ℕ => siftingSuccess n (signalCount gamma n))
Filter.atTop (nhds (1 : ℝ)) := by sorry
end
end QAlgorithms.Papers.Independent.Intakenext100.P17
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What the Lean code literally says, in plain math · gpt-6.1-sol
For every real number , put , the least nonnegative integer at least . The theorem asserts
Thus the fraction of ordered pairs of length- Boolean strings agreeing in at least coordinates tends to as the natural number tends to infinity.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.