One, Two, Three: One empirical evaluation of a two-copy shadow tomography scheme with triple efficiency

TL;DR

This paper empirically evaluates a novel triple-efficiency shadow tomography protocol for quantum states, confirming theoretical predictions and showing promising practical sample efficiency through classical simulations.

Contribution

It provides the first empirical assessment of triply efficient shadow tomography, validating theoretical scaling and improving a key subroutine with advanced convex optimization techniques.

Findings

Empirical sample complexity matches theoretical predictions for stabilizer states.

Slightly better scaling observed for random Gibbs states.

Constants involved in the protocol are relatively small, indicating practical efficiency.

Abstract

Shadow tomography protocols have recently emerged as powerful tools for efficient quantum state learning, aiming to reconstruct expectation values of observables with fewer resources than traditional quantum state tomography. For the particular case of estimating Pauli observables, entangling two-copy measurement schemes can offer an exponential improvement in sample complexity over any single-copy strategy conceivable [1, Huang, Kueng, Preskill, PRL(2021)]. A recent refinement of these ideas by King et al. [2, King, Gosset, Kothari, Babbush, SODA (2025)] does not only achieve polynomial sample complexity, but also maintains reasonable computational demands and utilizes joint measurements on only a small constant number of state copies. This `triple efficiency' is achievable for any subset of $n$-qubit Pauli observables, whereas single-copy strategies can only be efficient if the Pauli observables have advantageous structure. In this work, we complement existing theoretical performance guarantees with the empirical evaluation of triply efficient shadow tomography using classical, noise-free simulations. Our findings indicate that the empirical sample complexity aligns closely with theoretical predictions for stabilizer states and, notably, demonstrates slightly improved scaling for random Gibbs states compared to established theoretical bounds. In addition, we improve a central subroutine in the triply-efficient shadow protocol by leveraging insights from a refined quantum and quantum-inspired convex optimization algorithm [3, Henze et al. arXiv:2502.15426 (2025)]. To summarize, our empirical sample complexity studies of triply efficient shadow tomography not only confirm existing theoretical scaling behavior, but also showcase that the actual constants involved are comparatively benign. Hence, this protocol has the potential to also be very sample-efficient in practice.