Unbiased full Choi snapshots
OpenQAlgorithms.Papers.Independent.Intakethird100.P66.unbiasedChoiSnapshotsLet be any CPTP map on qubits with normalized reference-first Choi state . Fix an ideal Pauli-6, global Clifford, or fixed block-factorized measurement frame independently of . Draw a fresh uniform basis and its computational Born outcome on . Using the actual linear inverse measurement channel, form
Then
The global inverse acts in dimension , while the external factor is . 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.
import Definitions.Def_QAlgorithms_Papers_Independent_Intakethird100_P66
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
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What the Lean code literally says, in plain math · gpt-6.1-sol
For every natural number , every basis-choice frame on qubits satisfying the structural condition below, and every channel on -qubit matrices, the weighted mean of the scaled frame snapshots equals the unnormalized Choi matrix:
Here is the finite nonempty type of basis choices supplied by , is its matrix for basis choice and outcome string , paired strings are identified with their concatenation, and real weights are used as complex scalar coefficients. The map is the inverse of the complex linear map
when that map is injective, and is defined to be the zero linear map otherwise. The channel hypothesis requires a finite Kraus representation for every matrix , for some , with . The allowed structural condition on 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 qubits has choices , , , and . A global Clifford frame on qubits uses a supplied finite enumeration of the quotient of the unitary Pauli normalizer by equality of conjugation maps , chooses a representative of each class, and uses ; the normalized subgroup is generated by all -qubit Pauli tensors with phases . 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 .
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.