Signal-energy bound on the Chernoff exponent
OpenQAlgorithms.Papers.Independent.Intakethird100.P54.chernoff_exponentLet be the finite number of signal modes and the finite number of retained idler modes, on the full optical Fock spaces. The two channels have equal priors, independent vacuum environments, reflectances , field magnitudes and , and distinct complex reflection coefficients . Every signal mode sees the same channel; idlers are retained losslessly and environment outputs are inaccessible. Write for squared Uhlmann fidelity, for minimum average error over all joint binary receivers, , and , with zero powers interpreted as support projections. Here the common phase is removed, so and . Put .
Assume additionally , and let a normalized pure probe have finite mean signal energy . Then and . With natural logarithms and ,
For each positive integer , the normalized experiment consisting of independent copies also satisfies
These bounds control copy asymptotics; the identification of with the limiting optimal-error exponent is cited background, equation (55), rather than a new theorem of this paper.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P54
noncomputable section
open scoped BigOperators ComplexConjugate
open MeasureTheory Filter
namespace QAlgorithms.Papers.Independent.Intakethird100.P54
open QAlgorithms.RealRegisters (Hilbert)
open QAlgorithms.Papers.Independent.Claimed50.P47 (Occupation)
open QAlgorithms.Papers.Independent.Dependency50.P39 (coherentCoefficient)
attribute [local instance] Classical.propDecidable
attribute [local instance] Classical.decEq
/-- Chernoff exponent and finite-copy bound, PDF p.7, (56)–(57).
Natural logarithms are used; R1<1 excludes a log-zero/infinite-exponent endpoint.
The identification with the asymptotic opticalError exponent (55) is cited background [18]. -/
theorem chernoff_exponent {M J : ℕ} (hM : 0 < M) (c : Channels)
(hc : ValidSplitters c) (hr : Reading c) (hloss : (c true).reflectance < 1)
(psi : Hilbert (Occupation (Fin J) × Occupation (Fin M))) (hpsi : ‖psi‖=1)
(N : ℝ) (hN : HasSignalEnergy psi N) :
0 < splitterOverlap c ∧ splitterOverlap c < 1 ∧ 0 < outputChernoff c psi ∧
-Real.log (outputChernoff c psi) ≤ -2*N*Real.log (splitterOverlap c) ∧
∀ L : ℕ, 0 < L → ‖opticalCopies psi L‖=1 ∧
Real.rpow (splitterOverlap c) (2*N*(L : ℝ))/4 ≤ outputError c (opticalCopies psi L) := by sorry
end QAlgorithms.Papers.Independent.Intakethird100.P54
end
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What the Lean code literally says, in plain math · gpt-6.1-sol
Let with , and let the two splitters have reflectances , phases , , unequal fields , and ; additionally assume . Put , , and . Let have norm , and let be nonnegative with summable and , where . For any probe with signal modes, define if coordinatewise, and otherwise; its returned kernels are , where . A resolution of a kernel means , with , nonnegative summable , , unit for positive weights, and orthogonality between distinct vectors having positive weights; zero-weight vectors are unrestricted. For two kernels define
where (f_t(p)=0p=0f_t(p)=p^t00e(K_0,K_1)E\langle u,Ev\rangle=\overline{\langle v,Eu\rangle}0\le\operatorname{Re}\langle v,Ev\rangle\le|v|^2\frac12\sum_a p_a(1-\operatorname{Re}\langle u_a,Eu_a\rangle)+\frac12\sum_b q_b\operatorname{Re}\langle v_b,Ev_b\rangle00$ if not summable. Then
Moreover, for every natural number , form the coefficient function
and let be this function as an vector if it is square summable, and the zero vector otherwise. The assertion also gives
The statement allows , , and , but its conclusion about copies is only for .
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.