Likelihood-free methods for quantum parameter estimation

TL;DR

This paper introduces a likelihood-free quantum parameter estimation algorithm that leverages simulators producing samples rather than exact probabilities, enabling efficient characterization of large quantum systems beyond traditional methods.

Contribution

The authors develop an explicit likelihood-free algorithm for quantum parameter estimation using sample-based simulators, surpassing standard methods that require exact probability computations.

Findings

Algorithm exponentially outperforms traditional methods

Enables characterization of large quantum systems

Does not require exact probability calculations

Abstract

In this Letter, we strengthen and extend the connection between simulation and estimation to exploit simulation routines that do not exactly compute the probability of experimental data, known as the likelihood function. Rather, we provide an explicit algorithm for estimating parameters of physical models given access to a simulator which is only capable of producing sample outcomes. Since our algorithm does not require that a simulator be able to efficiently compute exact probabilities, it is able to exponentially outperform standard algorithms based on exact computation. In this way, our algorithm opens the door for the application of new insights and resources to the problem of characterizing large quantum systems, which is exponentially intractable using standard simulation resources.