Adversarial quantum circuit learning for pure state approximation

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Adversarial quantum circuit learning for pure state approximation

Benedetti, M; Grant, E; Wossnig, L; Severini, S; (2019) Adversarial quantum circuit learning for pure state approximation. New Journal Of Physics , 21 , Article 043023. 10.1088/1367-2630/ab14b5. Green open access

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Abstract

Adversarial learning is one of the most successful approaches to modeling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers. Two parametrized circuits are optimized in tandem: one tries to approximate the target state, the other tries to distinguish between target and approximated state. Supported by numerical simulations, we show that resilient backpropagation algorithms perform remarkably well in optimizing the two circuits. We use the bipartite entanglement entropy to design an efficient heuristic for the stopping criterion. Our approach may find application in quantum state tomography.

Type:	Article
Title:	Adversarial quantum circuit learning for pure state approximation
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1088/1367-2630/ab14b5
Publisher version:	https://doi.org/10.1088/1367-2630/ab14b5
Language:	English
Additional information:	Original content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. https://creativecommons.org/licenses/by/3.0/
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 Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10073776