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Unbiased full Choi snapshots

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QAlgorithms.Papers.Independent.Intakethird100.P66.unbiasedChoiSnapshots

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

prove2quantum

Let E\mathcal EE be any CPTP map on n≥1n\ge1n≥1 qubits with normalized reference-first Choi state ρΛ=2−nΛE\rho_{\Lambda}=2^{-n}\Lambda_{\mathcal E}ρΛ​=2−nΛE​. Fix an ideal Pauli-6, global Clifford, or fixed block-factorized measurement frame independently of E\mathcal EE. Draw a fresh uniform basis UUU and its computational Born outcome bbb on ρΛ\rho_{\Lambda}ρΛ​. Using the actual linear inverse measurement channel, form

Λ^=2nM−1(U†∣b⟩⟨b∣U).\widehat\Lambda=2^n\mathcal M^{-1}(U^\dagger|b\rangle\langle b|U).Λ=2nM−1(U†∣b⟩⟨b∣U).

Then

E[Λ^]=ΛE.\mathbb E[\widehat\Lambda]=\Lambda_{\mathcal E}.E[Λ]=ΛE​.

The global inverse acts in dimension 22n2^{2n}22n, while the external factor is 2n2^n2n. Individual snapshots may be indefinite and need not be CPTP or pure. This unbiased representation underlies linear prediction; no preservation of expectation by nonlinear postprocessing is asserted.

Preamble
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P66
Formal statement
noncomputable section

open MeasureTheory QAlgorithms ClassicalShadows

open scoped Matrix ComplexConjugate ENNReal

namespace QAlgorithms.Papers.Independent.Intakethird100.P66

attribute [local instance] Classical.propDecidable Classical.typeDecidableEq

/-- Section II, PDF p.2, Eq.(7), PDF p.4: one unbiased unnormalized Choi
snapshot for any CPTP process. A snapshot need not be positive, pure or CPTP. -/
theorem unbiasedChoiSnapshots (n : ℕ) (hn : 0 < n) (F : BasisChoiceFrame (n + n))
    (hF : PaperFrame F) (E : Superop (Qubits n) (Qubits n)) (hE : IsChannel E) :
    meanChoiSnapshot F E = processChoi E := by sorry

end QAlgorithms.Papers.Independent.Intakethird100.P66

end
Source
Classical Shadows for Quantum Process Tomography on Near-term Quantum Computers, arXiv:2110.02965v3, PDF p. 2, Section II; Eq.(7) on PDF p.4
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What the Lean code literally says, in plain math · gpt-6.1-sol

For every natural number n>0n>0n>0, every basis-choice frame FFF on 2n2n2n qubits satisfying the structural condition below, and every channel E\mathcal EE on nnn-qubit matrices, the weighted mean of the scaled frame snapshots equals the unnormalized Choi matrix:

∑a∈A∑b∈{0,1}2nRe⁡Tr⁡(Qa,bCE)∣A∣ 2nTF(Qa,b)=JE,(JE)(u,v),(u′,v′)=[E(∣u⟩⟨u′∣)]v,v′,CE=2−nJE.\sum_{a\in A}\sum_{b\in\{0,1\}^{2n}} \frac{\operatorname{Re}\operatorname{Tr}(Q_{a,b}C_{\mathcal E})}{|A|} \,2^n T_F(Q_{a,b}) =J_{\mathcal E}, \qquad (J_{\mathcal E})_{(u,v),(u',v')} =\bigl[\mathcal E(|u\rangle\langle u'|)\bigr]_{v,v'}, \qquad C_{\mathcal E}=2^{-n}J_{\mathcal E}.a∈A∑​b∈{0,1}2n∑​∣A∣ReTr(Qa,b​CE​)​2nTF​(Qa,b​)=JE​,(JE​)(u,v),(u′,v′)​=[E(∣u⟩⟨u′∣)]v,v′​,CE​=2−nJE​.

Here AAA is the finite nonempty type of basis choices supplied by FFF, Qa,bQ_{a,b}Qa,b​ is its matrix for basis choice aaa and outcome string bbb, paired strings are identified with their concatenation, and real weights are used as complex scalar coefficients. The map TFT_FTF​ is the inverse of the complex linear map

MF(X)=1∣A∣∑a∈A∑b∈{0,1}2nTr⁡(Qa,bX) Qa,b\mathcal M_F(X)=\frac1{|A|} \sum_{a\in A}\sum_{b\in\{0,1\}^{2n}} \operatorname{Tr}(Q_{a,b}X)\,Q_{a,b}MF​(X)=∣A∣1​a∈A∑​b∈{0,1}2n∑​Tr(Qa,b​X)Qa,b​

when that map is injective, and is defined to be the zero linear map otherwise. The channel hypothesis requires a finite Kraus representation E(X)=∑j=0r−1KjXKj†\mathcal E(X)=\sum_{j=0}^{r-1}K_jXK_j^\daggerE(X)=∑j=0r−1​Kj​XKj†​ for every matrix XXX, for some r∈Nr\in\mathbb Nr∈N, with ∑jKj†Kj=I\sum_jK_j^\dagger K_j=I∑j​Kj†​Kj​=I. The allowed structural condition on FFF is that it is either the Pauli frame, or a frame obtained by finitely tensoring global Clifford frames and the zero-qubit Pauli frame. The Pauli frame on mmm qubits has choices a=(Aj,sj)j<ma=(A_j,s_j)_{j<m}a=(Aj​,sj​)j<m​, Aj∈{X,Y,Z}A_j\in\{X,Y,Z\}Aj​∈{X,Y,Z}, sj∈{−1,1}s_j\in\{-1,1\}sj​∈{−1,1}, and Qa,b=⨂j(I+sj(−1)bjAj)/2Q_{a,b}=\bigotimes_j(I+s_j(-1)^{b_j}A_j)/2Qa,b​=⨂j​(I+sj​(−1)bj​Aj​)/2. A global Clifford frame on mmm qubits uses a supplied finite enumeration of the quotient of the unitary Pauli normalizer by equality of conjugation maps X↦UXU†X\mapsto UXU^\daggerX↦UXU†, chooses a representative UaU_aUa​ of each class, and uses Qa,b=Ua†∣b⟩⟨b∣UaQ_{a,b}=U_a^\dagger|b\rangle\langle b|U_aQa,b​=Ua†​∣b⟩⟨b∣Ua​; the normalized subgroup is generated by all mmm-qubit Pauli tensors with phases 1,i,−1,−i1,i,-1,-i1,i,−1,−i. Tensoring frames forms product basis-choice types and tensor products of their matrices, and the zero-qubit frame has one choice, one outcome, and matrix 111.

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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