Experimental quantum speed-up in reinforcement learning agents
Name
2103.06294.pdf
Description
Accepted version
Size
7.99 MB
Format
Adobe PDF
Checksum (MD5)
d0618ab2e9a2ce3f31d8e6f6157c0bf3
Author(s)
Saggio, V
Asenbeck, BE
Hamann, A
Strömberg, T
Schiansky, P
Dunjko, V
Friis, N
Harris, NC
Hochberg, M
Englund, D
Date Issued
2021
Journal
Nature
Publisher
Springer Science and Business Media LLC
Citation
Saggio, V, Asenbeck, BE, Hamann, A, Strömberg, T, Schiansky, P et al. 2021. "Experimental quantum speed-up in reinforcement learning agents." Nature, 591 (7849).
Version
Author's final manuscript
Abstract
© 2021, The Author(s), under exclusive licence to Springer Nature Limited. 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.
MIT Department
Massachusetts Institute of Technology. Department of Chemistry
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1038/S41586-021-03242-7
