Nearly query-optimal classical shadow estimation of unitary channels

TL;DR

This paper introduces a nearly optimal quantum shadow estimation protocol for learning properties of unknown unitary channels, significantly reducing the number of queries needed compared to previous methods.

Contribution

It presents a query-efficient protocol for classical shadow estimation of unitary channels with quadratic improvement and near optimality, including a practical single-copy measurement variant.

Findings

Quadratic query complexity advantage over previous methods

Protocol nearly saturates the information-theoretic lower bound

Applicable to both linear and non-linear channel properties

Abstract

Classical shadow estimation (CSE) is a powerful tool for learning the properties of quantum states and quantum processes. Here we consider the CSE task for quantum unitary channels. By querying an unknown unitary channel $\mathcal{U}$ multiple times in quantum experiments, the goal is to learn a classical description from which one can accurately predict many different linear properties of the channel, i.e., the expectation values of arbitrary observables measured on the output of $\mathcal{U}$ upon arbitrary input states. Based on collective measurements on multiple systems, we propose a query efficient protocol for this task, whose query complexity has a quadratic advantage over the previous best approach for this problem, and almost saturates the information-theoretic lower bound. To further enhance practicality, we also present a variant protocol using only single-copy measurements, which still offers much better query performance than previous protocols that do not use quantum memory, and can serve as a key subroutine for learning an arbitrary unknown Hamiltonian from dynamics. In addition to linear properties of unitary channels, our protocol can also be applied to simultaneously predict many non-linear properties, such as out-of-time-ordered correlators.