Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner

0512077 - FZÚ 2020 RIV US eng J - Journal Article
Jašek, J. - Jiráková, K. - Bartkiewicz, K. - Černoch, Antonín - Fürst, T. - Lemr, K.
Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner.
Optics Express. Roč. 27, č. 22 (2019), s. 32454-32464. ISSN 1094-4087
Institutional support: RVO:68378271
Keywords : hybrid quantum-classical reinforcement * boson sampling * quantum cloner
OECD category: Optics (including laser optics and quantum optics)
Impact factor: 3.669, year: 2019
Method of publishing: Open access

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.

Permanent Link: http://hdl.handle.net/11104/0302290