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Machine-Learning Quantum States in the NISQ Era

Authors
  • Torlai, Giacomo
  • Melko, Roger G.
Type
Published Article
Journal
Annual Review of Condensed Matter Physics
Publisher
Annual Reviews
Publication Date
Mar 10, 2020
Volume
11
Pages
325–344
Identifiers
DOI: 10.1146/annurev-conmatphys-031119-050651
Source
Annual Reviews
Keywords
License
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Abstract

We review the development of generative modeling techniques in machine learning for the purpose of reconstructing real, noisy, many-qubit quantum states. Motivated by its interpretability and utility, we discuss in detail the theory of the restricted Boltzmann machine. We demonstrate its practical use for state reconstruction, starting from a classical thermal distribution of Ising spins, then moving systematically through increasingly complex pure and mixed quantum states. We review recent techniques in reconstruction of a cold atom wavefunction, intended for use on experimental noisy intermediate-scale quantum (NISQ) devices. Finally, we discuss the outlook for future experimental state reconstruction using machine learning in the NISQ era and beyond.

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