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Experimental quantum speed-up in reinforcement learning agents

Abstract

As the field of artificial intelligence advances, the demand for algorithms that can learn quickly and efficiently increases. An important paradigm within artificial intelligence is reinforcement learning1, where decision-making entities called agents interact with environments and learn by updating their behaviour on the basis of the obtained feedback. The crucial question for practical applications is how fast agents learn2. Although various studies have made use of quantum mechanics to speed up the agent’s decision-making process3,4, a reduction in learning time has not yet been demonstrated. Here we present a reinforcement learning experiment in which the learning process of an agent is sped up by using a quantum communication channel with the environment. We further show that combining this scenario with classical communication enables the evaluation of this improvement and allows optimal control of the learning progress. We implement this learning protocol on a compact and fully tunable integrated nanophotonic processor. The device interfaces with telecommunication-wavelength photons and features a fast active-feedback mechanism, demonstrating the agent’s systematic quantum advantage in a setup that could readily be integrated within future large-scale quantum communication networks.

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Fig. 1: Schematic of a learning agent.
Fig. 2: Experimental setup.
Fig. 3: Circuit implementation.
Fig. 4: Behaviour of the average reward η for different learning strategies.

Data availability

All the datasets used in the current work are available on Zenodo at https://doi.org/10.5281/zenodo.4327211.

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Acknowledgements

We thank L. A. Rozema, I. Alonso Calafell and P. Jenke for help with the detectors. A.H. acknowledges support from the Austrian Science Fund (FWF) through the project P 30937-N27. V.D. acknowledges support from the Dutch Research Council (NWO/OCW), as part of the Quantum Software Consortium programme (project number 024.003.037). N.F. acknowledges support from the Austrian Science Fund (FWF) through the project P 31339-N27. H.J.B. acknowledges support from the Austrian Science Fund (FWF) through SFB BeyondC F7102, the Ministerium für Wissenschaft, Forschung, und Kunst Baden-Württemberg (Az. 33-7533-30-10/41/1) and the Volkswagen Foundation (Az. 97721). P.W. acknowledges support from the research platform TURIS, the European Commission through ErBeStA (no. 800942), HiPhoP (no. 731473), UNIQORN (no. 820474), EPIQUS (no. 899368), and AppQInfo (no. 956071), from the Austrian Science Fund (FWF) through CoQuS (W1210-N25), BeyondC (F 7113) and Research Group (FG 5), and Red Bull GmbH. The MIT portion of the work was supported in part by AFOSR award FA9550-16-1-0391 and NTT Research.

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Authors

Contributions

V.S. and B.E.A. implemented the experiment and performed data analysis. A.H., V.D., N.F., S.W. and H.J.B. developed the theoretical idea. T.S. and P.S. provided help with the experimental implementation. N.C.H., M.H. and D.E. designed the nanophotonic processor. V.S., S.W. and P.W. supervised the project. All the authors contributed to writing the paper.

Corresponding authors

Correspondence to V. Saggio or P. Walther.

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The authors declare no competing interests.

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Peer review informationNature thanks Vojtěch Havlíček, Lucas Lamata and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Saggio, V., Asenbeck, B.E., Hamann, A. et al. Experimental quantum speed-up in reinforcement learning agents. Nature 591, 229–233 (2021). https://doi.org/10.1038/s41586-021-03242-7

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