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Quantum State Discrimination Using Noisy Quantum Neural Networks

Patterson, A; Chen, H; Wossnig, L; Severini, S; Browne, D; Rungger, I; (2021) Quantum State Discrimination Using Noisy Quantum Neural Networks. Physical Review Research , 3 , Article 013063. 10.1103/PhysRevResearch.3.013063. Green open access

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Abstract

Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, applicable on near-term quantum devices as it fulfils the above criteria. We find that when simulating gradient calculation on a noisy device, a large number of parameters is disadvantageous. By introducing a new smaller circuit ansatz we overcome this limitation, and find that the QNN performs well at noise levels of current quantum hardware. We also show that networks trained at higher noise levels can still converge to useful parameters. Our findings show that noisy quantum computers can be used in applications for state discrimination and for classifiers of the output of quantum generative adversarial networks.

Type: Article
Title: Quantum State Discrimination Using Noisy Quantum Neural Networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1103/PhysRevResearch.3.013063
Publisher version: http://dx.doi.org/10.1103/PhysRevResearch.3.013063
Language: English
Additional information: Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy
URI: https://discovery.ucl.ac.uk/id/eprint/10120269
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