Quantum neuromorphic computing

Marković, Danijela; Grollier, Julie

doi:10.1063/5.0020014

Quantum neuromorphic computing

Journal Article · Tue Oct 13 00:00:00 EDT 2020 · Applied Physics Letters

DOI:https://doi.org/10.1063/5.0020014· OSTI ID:1852977

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Univ. Paris-Saclay, Palaiseau (France). Unité Mixte de Physique CNRS/Thales; OSTI
Univ. Paris-Saclay, Palaiseau (France). Unité Mixte de Physique CNRS/Thales

Quantum neuromorphic computing physically implements neural networks in brain-inspired quantum hardware to speed up their computation. In this perspective article, we show that this emerging paradigm could make the best use of the existing and near future intermediate size quantum computers. Some approaches are based on parametrized quantum circuits and use neural network-inspired algorithms to train them. Other approaches, closer to classical neuromorphic computing, take advantage of the physical properties of quantum oscillator assemblies to mimic neurons and synapses to compute. In this work, we discuss the different implementations of quantum neuromorphic networks with digital and analog circuits, highlight their respective advantages, and review exciting recent experimental results.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of California, San Diego, CA (United States)

Sponsoring Organization:: European Research Council (ERC); USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0019273

OSTI ID:: 1852977

Alternate ID(s):: OSTI ID: 1772256

Journal Information:: Applied Physics Letters, Journal Name: Applied Physics Letters Journal Issue: 15 Vol. 117; ISSN 0003-6951

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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