English

Triply efficient shadow tomography

Quantum Physics 2025-03-28 v1

Abstract

Given copies of a quantum state ρ\rho, a shadow tomography protocol aims to learn all expectation values from a fixed set of observables, to within a given precision ϵ\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 ρ\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 GG 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 GG 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 nn-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 nn-qubit quantum state into a poly(n)\text{poly}(n)-sized classical representation, from which one can extract the expected value of any of the 4n4^n Pauli observables in poly(n)\text{poly}(n) time, up to a small constant error.

Keywords

Cite

@article{arxiv.2404.19211,
  title  = {Triply efficient shadow tomography},
  author = {Robbie King and David Gosset and Robin Kothari and Ryan Babbush},
  journal= {arXiv preprint arXiv:2404.19211},
  year   = {2025}
}
R2 v1 2026-06-28T16:10:40.072Z