One-sided broad-prior limit and TMSS covariance
OpenQAlgorithms.Papers.Independent.Intakethird100.P89.broadPriorLimitFix finite . For every use the normalized Gaussian prior, and let , and be the optimizer and independently defined scalar maximum. As through positive values,
The minimum-family coefficients (9), evaluated at , obey
These are the pure TMSS covariance and unit displacement gain. This expresses the broad-prior limit of normalized ensembles, without asserting a uniform probability measure on or substituting in (12). The source identifies the limiting fidelity with earlier results [17,29] and the unit-gain BK protocol [9].
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P89
noncomputable section
open MeasureTheory Filter
open scoped BigOperators ENNReal Topology ComplexOrder
attribute [local instance] Classical.propDecidable Classical.decEq
namespace QAlgorithms.Papers.Independent.Intakethird100.P89
open QAlgorithms.Papers.Independent.Dependency50.P33
(State IsDensity IsTraceClassChannel tensorApply traceSize)
open QAlgorithms.Papers.Independent.Dependency50.P39
(coherentKet coherentCoefficient operatorOfMatrix husimiQ HasFockMatrix CoherentRealized)
open QAlgorithms.Papers.Independent.Frontier30.P14 (weylCharacteristic)
open QAlgorithms.Papers.Independent.Claimed50.P19
(Cov FockIndex DensityKernel PhysicalCovariance CharacteristicSummable
multimodeWeylTrace covarianceCharacteristicValue standardFormCovariance)
open QAlgorithms.Papers.Independent.Frontier30.P08 (QuadratureIndex)
open QAlgorithms.Papers.Independent.Gap48.P26 (PhaseInsensitive coherentKernel)
open QAlgorithms.Entanglement (partialTransposeFirst)
/-- PDF p.4, (15): one-sided broad-prior limit of normalized lambda>0
ensembles and minimum-family covariances, not a uniform probability on C.
This recovers the cited earlier results [17,29] and unit-gain BK [9]. -/
theorem broadPriorLimit (r : ℝ) (hr : 0 ≤ r) :
Tendsto (fun lambda => optimalTau lambda r) (nhdsWithin 0 (Set.Ioi 0)) (𝓝 1) ∧
Tendsto (fun lambda => optimalNoise lambda r) (nhdsWithin 0 (Set.Ioi 0))
(𝓝 (2 * Real.exp (-2 * r))) ∧
Tendsto (fun lambda => scalarOptimum lambda r) (nhdsWithin 0 (Set.Ioi 0))
(𝓝 (1 / (1 + Real.exp (-2 * r)))) ∧
Tendsto (fun lambda => familyMinA r (optimalTau lambda r)) (nhdsWithin 0 (Set.Ioi 0))
(𝓝 (Real.cosh (2 * r))) ∧
Tendsto (fun lambda => familyMinB r (optimalTau lambda r)) (nhdsWithin 0 (Set.Ioi 0))
(𝓝 (Real.cosh (2 * r))) ∧
Tendsto (fun lambda => familyMinC r (optimalTau lambda r)) (nhdsWithin 0 (Set.Ioi 0))
(𝓝 (Real.sinh (2 * r))) ∧
Tendsto (fun lambda => Real.sqrt (optimalTau lambda r)) (nhdsWithin 0 (Set.Ioi 0)) (𝓝 1) := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P89
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
For every fixed real , define
and let be the real supremum of
Also put
As the real variable tends to zero through positive values, all seven limits hold:
These are one-sided limits and impose no assertion about the values at . They include . All displayed scalar quotients are total, with division by zero equal to zero; the real supremum convention assigns zero to an empty or unbounded-above set.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.