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Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner
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SYSNO ASEP 0512077 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner Author(s) Jašek, J. (CZ)
Jiráková, K. (CZ)
Bartkiewicz, K. (CZ)
Černoch, Antonín (FZU-D) RID, ORCID
Fürst, T. (CZ)
Lemr, K. (CZ)Number of authors 6 Source Title Optics Express. - : Optical Society of America - ISSN 1094-4087
Roč. 27, č. 22 (2019), s. 32454-32464Number of pages 11 s. Language eng - English Country US - United States Keywords hybrid quantum-classical reinforcement ; boson sampling ; quantum cloner Subject RIV BH - Optics, Masers, Lasers OECD category Optics (including laser optics and quantum optics) Method of publishing Open access Institutional support FZU-D - RVO:68378271 UT WOS 000492996000109 EID SCOPUS 85074357139 DOI 10.1364/OE.27.032454 Annotation We report on experimental implementation of a machine-learned quantum gate driven by a classical control. The gate learns optimal phase-covariant cloning in a reinforcement learning scenario having fidelity of the clones as reward. In our experiment, the gate learns to achieve nearly optimal cloning fidelity allowed for this particular class of states. This makes it a proof of present-day feasibility and practical applicability of the hybrid machine learning approach combining quantum information processing with classical control. The quantum information processing performed by the setup is equivalent to boson sampling, which, in complex systems, is predicted to manifest quantum supremacy over classical simulation of linear-optical setups.
Workplace Institute of Physics Contact Kristina Potocká, potocka@fzu.cz, Tel.: 220 318 579 Year of Publishing 2020 Electronic address http://hdl.handle.net/11104/0302290
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