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§III: general-input projection and conditional emulation

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QAlgorithms.Papers.Independent.Intakethird100.P67.emulator_projection_limit

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

prove2quantum

Let normalized pure samples, K≥2K\ge2K≥2, generate the full algebra on their span, and let Π\PiΠ be its projector. Fix an anchor and an unknown unitary UUU. In Fig.2 take the reconstructed system on b=0b=0b=0 and the old system from the SWAP on b=1b=1b=1. Let FT,b\mathcal F_{T,b}FT,b​ be these averaged subnormalized output branches. For every input density operator,

lim⁡T→∞∥FT,0(ρ)−UΠρΠU†∥1=0,lim⁡T→∞∥FT,1(ρ)−(I−Π)ρ(I−Π)∥1=0.\lim_{T\to\infty}\|\mathcal F_{T,0}(\rho)-U\Pi\rho\Pi U^\dagger\|_1=0,\qquad\lim_{T\to\infty}\|\mathcal F_{T,1}(\rho)-(I-\Pi)\rho(I-\Pi)\|_1=0.T→∞lim​∥FT,0​(ρ)−UΠρΠU†∥1​=0,T→∞lim​∥FT,1​(ρ)−(I−Π)ρ(I−Π)∥1​=0.

This states projection followed by conditional emulation on the successful branch, including zero Born probability without division. The circuit and its algebra promise differ from Appendix H's unknown-projector instrument.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P67
Formal statement
noncomputable section

open scoped ComplexConjugate Matrix Matrix.Norms.L2Operator

attribute [local instance] Classical.propDecidable

open MeasureTheory Filter

namespace QAlgorithms.Papers.Independent.Intakethird100.P67

variable {I J K L A : Type*} [Fintype I] [DecidableEq I]
  [Fintype J] [DecidableEq J] [Fintype K] [DecidableEq K]
  [Fintype L] [DecidableEq L] [Fintype A] [DecidableEq A]

/-- PDF pp.4,6: general-input Fig.2 instrument converges to conditional emulation
on H_in and projection on its complement, using the actual outcome-dependent wires. -/
theorem emulator_projection_limit (phi : K → EuclideanSpace ℂ I) (a : K)
    (hK : 2 ≤ Fintype.card K) (hphi : ∀ k, IsState (phi k))
    (hgen : Generates (pureSamples phi)) (U : Matrix.unitaryGroup I ℂ)
    (rho : Matrix I I ℂ) (hrho : IsDensityMatrix rho) :
    let Pi := samplePi (pureSamples phi)
    Tendsto (fun T : ℕ => traceNorm (wireBranch phi a (U : Matrix I I ℂ) T false rho -
      densityConjugation (U : Matrix I I ℂ) (Pi * rho * Pi))) atTop (nhds 0) ∧
    Tendsto (fun T : ℕ => traceNorm (wireBranch phi a (U : Matrix I I ℂ) T true rho -
      (1-Pi) * rho * (1-Pi))) atTop (nhds 0) := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P67

end
Source
Universal Quantum Emulator, arXiv:1606.02734v2, PDF p. 4, §III: general-input projection and conditional emulation
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What the Lean code literally says, in plain math · gpt-6.1-sol

Let I,KI,KI,K be finite index types with decidable equality, let a∈Ka\in Ka∈K, assume ∣K∣≥2|K|\ge2∣K∣≥2, and let every ϕk∈CI\phi_k\in\mathbb C^Iϕk​∈CI have norm 111. Put Ek=ϕkϕk†E_k=\phi_k\phi_k^\daggerEk​=ϕk​ϕk†​, and let Π=f(∑kEk)\Pi=f(\sum_kE_k)Π=f(∑k​Ek​) by continuous functional calculus, where f(0)=0f(0)=0f(0)=0 and f(x)=1f(x)=1f(x)=1 otherwise. Assume that, for every complex matrix XXX on III, membership in the complex linear span of all nonempty products Ek1⋯EkrE_{k_1}\cdots E_{k_r}Ek1​​⋯Ekr​​ is equivalent to ΠXΠ=X\Pi X\Pi=XΠXΠ=X. Let UUU be unitary and let ρ\rhoρ be positive semidefinite with trace 111. Define pa=0p_a=0pa​=0, pk=(∣K∣−1)−1p_k=(|K|-1)^{-1}pk​=(∣K∣−1)−1 for k≠ak\ne ak=a, Rk=1−2EkR_k=1-2E_kRk​=1−2Ek​, and Wk=C(Rk)(1⊗HHad)C(Ra)W_k=C(R_k)(1\otimes H_{\rm Had})C(R_a)Wk​=C(Rk​)(1⊗HHad​)C(Ra​), where C(M)C(M)C(M) applies MMM when the Boolean ancilla is true and the identity otherwise, and HHad=2−1/2(111−1)H_{\rm Had}=2^{-1/2}\begin{pmatrix}1&1\\1&-1\end{pmatrix}HHad​=2−1/2(11​1−1​). For each natural TTT and history h:{0,…,T−1}→Kh:\{0,\ldots,T-1\}\to Kh:{0,…,T−1}→K, let VhV_hVh​ successively apply Wh(j)W_{h(j)}Wh(j)​ to the system and ancilla jjj, and let V~h\widetilde V_hVh​ do the same with (U⊗1)Wh(j)(U†⊗1)(U\otimes1)W_{h(j)}(U^\dagger\otimes1)(U⊗1)Wh(j)​(U†⊗1). Write ωT=∣−⟩⟨−∣⊗T\omega_T=|-\rangle\langle-|^{\otimes T}ωT​=∣−⟩⟨−∣⊗T, Xh=Vh(ρ⊗ωT)Vh†X_h=V_h(\rho\otimes\omega_T)V_h^\daggerXh​=Vh​(ρ⊗ωT​)Vh†​, wh=∏jph(j)w_h=\prod_jp_{h(j)}wh​=∏j​ph(j)​, and Ea′=(Uϕa)(Uϕa)†E'_a=(U\phi_a)(U\phi_a)^\daggerEa′​=(Uϕa​)(Uϕa​)†. The two matrices whose limits are asserted are

FT=∑hwh tr⁡anc ⁣[V~h†(Ea′⊗tr⁡sys((Ea⊗1)Xh(Ea⊗1)))V~h],NT=∑hwh tr⁡anc ⁣[((1−Ea)⊗1)Xh((1−Ea)⊗1)].\begin{aligned} F_T&=\sum_h w_h\,\operatorname{tr}_{\rm anc} \!\left[\widetilde V_h^\dagger \left(E'_a\otimes \operatorname{tr}_{\rm sys}((E_a\otimes1)X_h(E_a\otimes1))\right) \widetilde V_h\right],\\ N_T&=\sum_h w_h\,\operatorname{tr}_{\rm anc} \!\left[((1-E_a)\otimes1)X_h((1-E_a)\otimes1)\right]. \end{aligned}FT​NT​​=h∑​wh​tranc​[Vh†​(Ea′​⊗trsys​((Ea​⊗1)Xh​(Ea​⊗1)))Vh​],=h∑​wh​tranc​[((1−Ea​)⊗1)Xh​((1−Ea​)⊗1)].​

With ∥X∥tr=Re⁡tr⁡X†X\|X\|_{\rm tr}=\operatorname{Re}\operatorname{tr}\sqrt{X^\dagger X}∥X∥tr​=RetrX†X​, the conclusion is

lim⁡T→∞∥FT−U(ΠρΠ)U†∥tr=0,lim⁡T→∞∥NT−(1−Π)ρ(1−Π)∥tr=0.\lim_{T\to\infty}\|F_T-U(\Pi\rho\Pi)U^\dagger\|_{\rm tr}=0, \qquad \lim_{T\to\infty}\|N_T-(1-\Pi)\rho(1-\Pi)\|_{\rm tr}=0.T→∞lim​∥FT​−U(ΠρΠ)U†∥tr​=0,T→∞lim​∥NT​−(1−Π)ρ(1−Π)∥tr​=0.

The histories and products are empty at T=0T=0T=0; no support condition on ρ\rhoρ is assumed.

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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