Compressing measurements in quantum dynamic parameter estimation

TL;DR

This paper introduces a method combining classical compressive sensing with quantum control to exponentially reduce resources needed for dynamic quantum parameter estimation, applicable to quantum sensing and magnetometry.

Contribution

It proposes a novel approach that uses random measurement matrices generated by simple control sequences to efficiently estimate sparse, time-dependent parameters in quantum systems.

Findings

Exponential resource savings demonstrated in numerical examples.

Random measurement matrices satisfy restricted isometry property.

Applicable to quantum sensing and magnetometry.

Abstract

We present methods that can provide an exponential savings in the resources required to perform dynamic parameter estimation using quantum systems. The key idea is to merge classical compressive sensing techniques with quantum control methods to efficiently estimate time-dependent parameters in the system Hamiltonian. We show that incoherent measurement bases and, more generally, suitable random measurement matrices can be created by performing simple control sequences on the quantum system. Since random measurement matrices satisfying the restricted isometry property can be used to reconstruct any sparse signal in an efficient manner, and many physical processes are approximately sparse in some basis, these methods can potentially be useful in a variety of applications such as quantum sensing and magnetometry. We illustrate the theoretical results throughout the presentation with various practically relevant numerical examples.