Fisher Information in Noisy Intermediate-Scale Quantum Applications
Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany
QMATH, Department of Mathematical Sciences, Københavns Universitet, 2100 København Ø, Denmark
| Published: | 2021-09-09, volume 5, page 539 |
| Eprint: | arXiv:2103.15191v3 |
| Doi: | https://doi.org/10.22331/q-2021-09-09-539 |
| Citation: | Quantum 5, 539 (2021). |
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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.
Featured image: Changes in parameters result in changes of the underlying parametrized quantum states and associated measurement distributions. Here, large changes in parameter space need not contribute to large changes of the underlying states and measurement distributions. This motivates the use of distances between states and measurement distributions as distance measures between parameters.
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