Machine learning quantum states in the NISQ era

Giacomo Torlai; Roger G. Melko

doi:10.1146/annurev-conmatphys-031119-050651

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arXiv · 1905.04312

Machine learning quantum states in the NISQ era

Giacomo Torlai·Roger G. Melko

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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. Intended for use on experimental noisy intermediate-scale quantum (NISQ) devices, we review recent efforts in reconstruction of a cold atom wavefunction. Finally, we discuss the outlook for future experimental state reconstruction using machine learning, in the NISQ era and beyond.

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quant-ph cond-mat.dis-nn cond-mat.quant-gas cond-mat.str-el

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BibTeX RIS

Giacomo Torlai, Roger G. Melko. 2019-05-10. Machine learning quantum states in the NISQ era. https://doi.org/10.1146/annurev-conmatphys-031119-050651

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