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Annual Review of Condensed Matter Physics

Volume 11, 2020

Machine-Learning Quantum States in the NISQ Era

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.

Keyword(s): condensed matter, information theory, neural networks, quantum physics, quantum state tomography, restricted Boltzmann machines
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/content/journals/10.1146/annurev-conmatphys-031119-050651
2020-03-10
2024-04-23
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/content/journals/10.1146/annurev-conmatphys-031119-050651
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Machine-Learning Quantum States in the NISQ Era
Annual Review of Condensed Matter Physics 11, 325 (2020); https://doi.org/10.1146/annurev-conmatphys-031119-050651
/content/journals/10.1146/annurev-conmatphys-031119-050651
/content/journals/10.1146/annurev-conmatphys-031119-050651
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