Likelihood-free methods for quantum parameter estimation

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.

Significantly revised presentation

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Cited by in corpus (9)

• Hamiltonian Learning and Certification Using Quantum Resources
• Efficient Bayesian Phase Estimation
• Accelerated Randomized Benchmarking
• QInfer: Statistical inference software for quantum applications
• Quantum Bootstrapping via Compressed Quantum Hamiltonian Learning
• Pseudo-Bayesian Quantum Tomography with Rank-adaptation
• Quantum Model Averaging
• Maximum likelihood versus likelihood-free quantum system identification in the atom maser
• Efficient adaptive Bayesian estimation of a slowly fluctuating Overhauser field gradient