Fisher Information in Noisy Intermediate-Scale Quantum Applications

TL;DR

This paper explores how classical and quantum Fisher information can be used to analyze and improve noisy intermediate-scale quantum devices, especially in variational algorithms and quantum machine learning, emphasizing their potential beyond quantum sensing.

Contribution

It provides an accessible tutorial and review of Fisher information's role in noisy quantum systems, highlighting new applications in variational algorithms and quantum machine learning.

Findings

Fisher information can be calculated on near-term quantum devices.

Noise impacts Fisher information and its utility in quantum applications.

Fisher information is a versatile tool beyond quantum sensing.

Abstract

The recent advent of noisy intermediate-scale quantum devices, especially near-term quantum computers, has sparked extensive research efforts concerned with their possible applications. At the forefront of the considered approaches are variational methods that use parametrized quantum circuits. The classical and quantum Fisher information are firmly rooted in the field of quantum sensing and have proven to be versatile tools to study such parametrized quantum systems. Their utility in the study of other applications of noisy intermediate-scale quantum devices, however, has only been discovered recently. Hoping to stimulate more such applications, this article aims to further popularize classical and quantum Fisher information as useful tools for near-term applications beyond quantum sensing. We start with a tutorial that builds an intuitive understanding of classical and quantum Fisher information and outlines how both quantities can be calculated on near-term devices. We also elucidate their relationship and how they are influenced by noise processes. Next, we give an overview of the core results of the quantum sensing literature and proceed to a comprehensive review of recent applications in variational quantum algorithms and quantum machine learning.