Machine-Learning Quantum States in the NISQ Era

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 u...

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Bibliographic Details
Published inAnnual review of condensed matter physics Vol. 11; no. 1; pp. 325 - 344
Main Authors Torlai, Giacomo, Melko, Roger G
Format Journal Article
LanguageEnglish
Published Annual Reviews 10.03.2020
Subjects
condensed matter
information theory
neural networks
quantum physics
quantum state tomography
restricted Boltzmann machines
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Summary: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.
ISSN:1947-5454
1947-5462
DOI:10.1146/annurev-conmatphys-031119-050651