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NDS Chernoff coefficients including support endpoints

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QAlgorithms.Papers.Independent.Intakethird100.P54.nds_chernoff

by Gohan · Nov 1, 2026 · Mathlib 0df444a (Lean v4.33.1)

prove2quantum

Let M≥1M\ge1M≥1 be the finite number of signal modes and J≥0J\ge0J≥0 the finite number of retained idler modes, on the full optical Fock spaces. The two channels have equal priors, independent vacuum environments, reflectances 0≤R0≤R1≤10\le R_0\le R_1\le10≤R0​≤R1​≤1, field magnitudes rb=Rbr_b=\sqrt{R_b}rb​=Rb​​ and tb=1−Rbt_b=\sqrt{1-R_b}tb​=1−Rb​​, and distinct complex reflection coefficients rbeiθbr_b e^{i\theta_b}rb​eiθb​. Every signal mode sees the same channel; idlers are retained losslessly and environment outputs are inaccessible. Write F\mathcal FF for squared Uhlmann fidelity, PeP_ePe​ for minimum average error over all joint binary receivers, Qs=Tr⁡(ρ0sρ11−s)Q_s=\operatorname{Tr}(\rho_0^s\rho_1^{1-s})Qs​=Tr(ρ0s​ρ11−s​), and Q=min⁡0≤s≤1QsQ=\min_{0\le s\le1}Q_sQ=min0≤s≤1​Qs​, with zero powers interpreted as support projections.

For a normalized pure NDS probe, let pb,kp_{b,\mathbf k}pb,k​ and IkI_{\mathbf k}Ik​ denote its actual loss-sector probabilities and overlaps. For every common exponent s∈[0,1]s\in[0,1]s∈[0,1] the following series converges:

Qs=∑k: p0,kp1,k>0p0,ks−1p1,k−s∣Ik∣2,Pe≤Qs2.Q_s=\sum_{\mathbf k:\,p_{0,\mathbf k}p_{1,\mathbf k}>0}p_{0,\mathbf k}^{s-1}p_{1,\mathbf k}^{-s}|I_{\mathbf k}|^2,\qquad P_e\le\frac{Q_s}{2}.Qs​=k:p0,k​p1,k​>0∑​p0,ks−1​p1,k−s​∣Ik​∣2,Pe​≤2Qs​​.

Sectors with either probability zero contribute zero. There exists s∗∈[0,1]s_*\in[0,1]s∗​∈[0,1] attaining Q=Qs∗Q=Q_{s_*}Q=Qs∗​​, and Pe≤Q/2P_e\le Q/2Pe​≤Q/2. In particular, endpoint powers are support projections, so the formula does not replace Q0Q_0Q0​ or Q1Q_1Q1​ by one.

The expression yields computable Chernoff bounds from the same NDS sector data used for exact discrimination.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P54
Formal statement
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

/-- NDS Chernoff-type formula with support endpoints and omitted empty sectors,
PDF p.6, (45), together with (7)–(9). The optimizer is concluded, not assumed. -/
theorem nds_chernoff {M J : ℕ} (hM : 0 < M) (c : Channels) (hc : ValidSplitters c)
    (psi : Hilbert (Occupation (Fin J) × Occupation (Fin M))) (hpsi : ‖psi‖ = 1)
    (hnds : IsNDS psi) :
    (∀ s ∈ Set.Icc (0 : ℝ) 1, Summable (ndsChernoffTerm c psi s) ∧
      outputChernoffAt c psi s = ∑' k, ndsChernoffTerm c psi s k ∧
      outputError c psi ≤ outputChernoffAt c psi s / 2) ∧
    (∃ s ∈ Set.Icc (0 : ℝ) 1, outputChernoff c psi = outputChernoffAt c psi s ∧
      ∀ t ∈ Set.Icc (0 : ℝ) 1, outputChernoffAt c psi s ≤ outputChernoffAt c psi t) ∧
    outputError c psi ≤ outputChernoff c psi / 2 := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P54

end
Source
Discriminating quantum-optical beam-splitter channels with number-diagonal signal states: Applications to quantum reading and target detection, arXiv:1105.4063v3, PDF p. 6, nds-chernoff
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What the Lean code literally says, in plain math · gpt-6.1-sol

Let M,J∈NM,J\in\mathbb NM,J∈N with M>0M>0M>0, allowing J=0J=0J=0, and let ψ∈H=ℓ2(NJ×NM;C)\psi\in H=\ell^2(\mathbb N^J\times\mathbb N^M;\mathbb C)ψ∈H=ℓ2(NJ×NM;C) have norm 111 and satisfy ∑iψ(i,n)ψ(i,q)‾=0\sum_i\psi(i,n)\overline{\psi(i,q)}=0∑i​ψ(i,n)ψ(i,q)​=0 whenever n≠qn\ne qn=q. For b∈{0,1}b\in\{0,1\}b∈{0,1}, let Rb∈[0,1]R_b\in[0,1]Rb​∈[0,1], θb∈R\theta_b\in\mathbb Rθb​∈R, rb=Rbr_b=\sqrt{R_b}rb​=Rb​​, and tb=1−Rbt_b=\sqrt{1-R_b}tb​=1−Rb​​, with R0≤R1R_0\le R_1R0​≤R1​ and r0eiθ0≠r1eiθ1r_0e^{i\theta_0}\ne r_1e^{i\theta_1}r0​eiθ0​=r1​eiθ1​. Define Lb(n,k)=∏m(nmkm)einmθbrbnm−kmtbkmL_b(n,k)=\prod_m\sqrt{\binom{n_m}{k_m}}e^{in_m\theta_b}r_b^{n_m-k_m}t_b^{k_m}Lb​(n,k)=∏m​(km​nm​​)​einm​θb​rbnm​−km​​tbkm​​ for k≤nk\le nk≤n componentwise, and zero otherwise; put gb,k(i,p)=ψ(i,p+k)Lb(p+k,k)g_{b,k}(i,p)=\psi(i,p+k)L_b(p+k,k)gb,k​(i,p)=ψ(i,p+k)Lb​(p+k,k), let vb,k∈Hv_{b,k}\in Hvb,k​∈H equal this function when square-summable and zero otherwise, and set ρb(x,y)=∑kgb,k(x)gb,k(y)‾\rho_b(x,y)=\sum_k g_{b,k}(x)\overline{g_{b,k}(y)}ρb​(x,y)=∑k​gb,k​(x)gb,k​(y)​, Pb,k=∥vb,k∥2P_{b,k}=\|v_{b,k}\|^2Pb,k​=∥vb,k​∥2, and Ck=⟨v0,k,v1,k⟩C_k=\langle v_{0,k},v_{1,k}\rangleCk​=⟨v0,k​,v1,k​⟩. A resolution (an,un)(a_n,u_n)(an​,un​) of a kernel KKK means summable nonnegative weights of sum 111, vectors unit at positive weights and orthogonal at distinct positive weights, and K(x,y)=∑nanun(x)un(y)‾K(x,y)=\sum_n a_nu_n(x)\overline{u_n(y)}K(x,y)=∑n​an​un​(x)un​(y)​, with zero-weight vectors unrestricted. Put h(p,t)=0h(p,t)=0h(p,t)=0 when p=0p=0p=0, and h(p,t)=pth(p,t)=p^th(p,t)=pt, using real powers, otherwise. Define

Qs=sup⁡(a,u),(b,w)∑n,m∈Nh(an,s)h(bm,1−s)∣⟨un,wm⟩∣2,Q=inf⁡{Qs:0≤s≤1},Q_s=\sup_{(a,u),(b,w)} \sum_{n,m\in\mathbb N}h(a_n,s)h(b_m,1-s) |\langle u_n,w_m\rangle|^2,\qquad Q=\inf\{Q_s:0\le s\le1\},Qs​=(a,u),(b,w)sup​n,m∈N∑​h(an​,s)h(bm​,1−s)∣⟨un​,wm​⟩∣2,Q=inf{Qs​:0≤s≤1},

where the resolutions are of ρ0,ρ1\rho_0,\rho_1ρ0​,ρ1​. Define EoutE_{\mathrm{out}}Eout​ as the infimum, over these resolutions and bounded complex-linear effects EEE, of 12∑nan(1−qE(un))+12∑nbnqE(wn)\tfrac12\sum_n a_n(1-q_E(u_n))+\tfrac12\sum_n b_nq_E(w_n)21​∑n​an​(1−qE​(un​))+21​∑n​bn​qE​(wn​), where qE(v)=Re⁡⟨v,Ev⟩q_E(v)=\operatorname{Re}\langle v,Ev\rangleqE​(v)=Re⟨v,Ev⟩, ⟨u,Ev⟩=⟨v,Eu⟩‾\langle u,Ev\rangle=\overline{\langle v,Eu\rangle}⟨u,Ev⟩=⟨v,Eu⟩​, and 0≤qE(v)≤∥v∥20\le q_E(v)\le\|v\|^20≤qE​(v)≤∥v∥2 for every vector. The real suprema and infima are total, taking value zero on empty defining sets. For each sss, put

Ds,k={0,P0,k=0 or P1,k=0,P0,k s−1P1,k−s∣Ck∣2,otherwise.D_{s,k}= \begin{cases} 0,&P_{0,k}=0\text{ or }P_{1,k}=0,\\ P_{0,k}^{\,s-1}P_{1,k}^{-s}|C_k|^2,&\text{otherwise}. \end{cases}Ds,k​={0,P0,ks−1​P1,k−s​∣Ck​∣2,​P0,k​=0 or P1,k​=0,otherwise.​

The theorem asserts that for every s∈[0,1]s\in[0,1]s∈[0,1], the sequence k↦Ds,kk\mapsto D_{s,k}k↦Ds,k​ is summable, Qs=∑kDs,kQ_s=\sum_kD_{s,k}Qs​=∑k​Ds,k​, and Eout≤Qs/2E_{\mathrm{out}}\le Q_s/2Eout​≤Qs​/2; that some s∈[0,1]s\in[0,1]s∈[0,1] satisfies Q=QsQ=Q_sQ=Qs​ and Qs≤QtQ_s\le Q_tQs​≤Qt​ for every t∈[0,1]t\in[0,1]t∈[0,1]; and that Eout≤Q/2E_{\mathrm{out}}\le Q/2Eout​≤Q/2. Both endpoints s=0,1s=0,1s=0,1 are included, and a zero resolution weight contributes zero even when its exponent is zero.

Human review
  • Endorsed by Shuze Chen · Nov 1, 2026

    Confirmed by the moderator at approval.

  • Endorsed by Gohan · Nov 1, 2026

    Confirmed by the mission captain (proposal self-audit).

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