Explicit sufficient reduced-process sample counts
OpenQAlgorithms.Papers.Independent.Intakethird100.P66.simultaneousReducedExplicitLet , and . Let be the trace-one reference-first Choi state of any CPTP channel. For a -element physical subsystem , let retain both copies of . Set , , and
Here ranges over the phase-positive -qubit tensor Paulis and extends by the identity to all wires. For a fixed informationally complete measurement primitive , the following sufficient total counts are rounded upward:
For each clause, there exists an estimator independent of the channel, taking exactly the indicated number of independent fresh-shot records, for which
using that clause's norm. The trace norm has no factor . At return scalar without samples. The reduced channel has a maximally mixed complementary input; full tomography is .
These explicit sufficient counts refine II.1. They are counts of all observations, not lengths of individual median-of-means batches. For the general Borel measurement primitive, the estimator is measurable, so the simultaneous success event is measurable.
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
/-- Theorem A.3(i),(ii), PDF p.14, A5-A9. Ambient Pauli embeddings use 2n
wires and dimension 4^n; identity coefficients are exact. The estimator-line
O^(k) is corrected to O^(2k). Real sufficient prefactors are rounded upward
as total shots, not batch lengths. -/
theorem simultaneousReducedExplicit [∀ m : ℕ, Fintype (Clifford.ProjectiveGroup m)]
(n k : ℕ) (hn : 0 < n) (hk : k ≤ n) (eps delta : ℝ)
(heps : 0 < eps) (hepsOne : eps < 1) (hdelta : 0 < delta) (hdeltaOne : delta < 1) :
(∀ P : MeasurementPrimitive (n + n), PrimitiveReducedRecovers P k
(sampleCount k (68 * (4 : ℝ) ^ k / eps ^ 2 * reducedLog n k delta *
reducedVariance P k)) eps delta) ∧
ReducedRecovers (globalFrame (n + n)) k
(sampleCount k (204 * (4 : ℝ) ^ (n + k) / eps ^ 2 * reducedLog n k delta))
.frobenius eps delta ∧
ReducedRecovers (pauliFrame (n + n)) k
(sampleCount k (68 * (36 : ℝ) ^ k / eps ^ 2 * reducedLog n k delta *
pauliNormEnvelope k)) .frobenius eps delta ∧
ReducedRecovers (pauliFrame (n + n)) k
(sampleCount k ((8 / 3 : ℝ) * (144 : ℝ) ^ k / eps ^ 2 * traceSampleLog n k delta))
.trace eps delta := 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
Assume that for every the quotient of the -qubit Pauli normalizer by equality of conjugation maps has a supplied finite enumeration, where the Pauli subgroup is generated by tensor products of Pauli matrices with phases . For every satisfying and , and every real satisfying and , four simultaneous recovery assertions hold with the explicit sample counts below. For their meaning, a channel is any map with finitely many Kraus matrices and , its normalized Choi matrix is
and for each injection its reduced matrix is
where the bracket notation means overwriting the indicated coordinates. Each recovery assertion means that there exists an estimator from the specified number of records and an injection to a complex matrix on qubits, such that for every channel the event has measure or finite record weight at least ; the estimator need not satisfy any stated measurability, positivity, or normalization condition. Set
The first assertion holds for every measurement primitive on qubits, in Frobenius norm, with
Such a primitive consists of a probability measure on unitaries with injective linear measurement channel
The record measure is the product of the single-record measures with density against , summed over . To define , for every as above and every -qubit Pauli tensor , extend to an observable on the input and output -qubit registers by using in both registers and the identity on the remaining wires: its matrix entry is at the restricted pair of strings when both registers agree outside the image of , and zero otherwise. Then is the maximum of over all such , where, for Hermitian , with ,
and for non-Hermitian ; a maximum over an empty finite index type is defined as zero. The remaining three assertions use finite frames: a frame with matrices assigns a record the weight
and assigns an event the sum of these weights. The global frame uses quotient classes of normalizer unitaries as basis choices and for chosen representatives; it recovers in Frobenius norm with
The Pauli frame uses choices , where , , and ; it recovers in Frobenius norm with
and in trace norm with
Here the trace norm is . The four assertions can have different estimators. The case is included, and all four sample counts are then exactly zero.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.