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Triply efficient shadow tomography

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arxiv 2404.19211 v1 pith:NLA6HOKV submitted 2024-04-30 quant-ph

Triply efficient shadow tomography

classification quant-ph
keywords measurementsobservablesshadowtomographyefficienttriplyprotocolgive
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Given copies of a quantum state $\rho$, a shadow tomography protocol aims to learn all expectation values from a fixed set of observables, to within a given precision $\epsilon$. We say that a shadow tomography protocol is triply efficient if it is sample- and time-efficient, and only employs measurements that entangle a constant number of copies of $\rho$ at a time. The classical shadows protocol based on random single-copy measurements is triply efficient for the set of local Pauli observables. This and other protocols based on random single-copy Clifford measurements can be understood as arising from fractional colorings of a graph $G$ that encodes the commutation structure of the set of observables. Here we describe a framework for two-copy shadow tomography that uses an initial round of Bell measurements to reduce to a fractional coloring problem in an induced subgraph of $G$ with bounded clique number. This coloring problem can be addressed using techniques from graph theory known as chi-boundedness. Using this framework we give the first triply efficient shadow tomography scheme for the set of local fermionic observables, which arise in a broad class of interacting fermionic systems in physics and chemistry. We also give a triply efficient scheme for the set of all $n$-qubit Pauli observables. Our protocols for these tasks use two-copy measurements, which is necessary: sample-efficient schemes are provably impossible using only single-copy measurements. Finally, we give a shadow tomography protocol that compresses an $n$-qubit quantum state into a $\text{poly}(n)$-sized classical representation, from which one can extract the expected value of any of the $4^n$ Pauli observables in $\text{poly}(n)$ time, up to a small constant error.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Online Shadow Tomography Matching the Classical Bounds

    quant-ph 2026-07 conditional novelty 7.0

    Online shadow tomography can be solved with O(log m sqrt(log d)/eps^3) or O(sqrt(m)/eps^2) copies, matching known classical rates, but the first bound's key proof lemma contains an invalid inequality.