Quantum speed-ups in reinforcement learning

Valeria Saggio; Beate E. Asenbeck; Arne Hamann; Teodor Strömberg; Peter Schiansky; Vedran Dunjko; Nicolai Friis; Nicholas C. Harris; Michael Hochberg; Dirk Englund; Sabine Wölk; Hans J. Briegel; Philip Walther

doi:10.1117/12.2593720

1 August 2021 Quantum speed-ups in reinforcement learning

Valeria Saggio, Beate E. Asenbeck, Arne Hamann, Teodor Strömberg, Peter Schiansky, Vedran Dunjko, Nicolai Friis, Nicholas C. Harris, Michael Hochberg, Dirk Englund, Sabine Wölk, Hans J. Briegel, Philip Walther

Author Affiliations +

Proceedings Volume 11806, Quantum Nanophotonic Materials, Devices, and Systems 2021; 118060N (2021) https://doi.org/10.1117/12.2593720
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

As the field of artificial intelligence is pushed forward, the question arises of how fast autonomous machines can learn. Within artificial intelligence, an important paradigm is reinforcement learning, where agents - learning entities capable of decision making - interact with the world they are placed in, called an environment. Thanks to these interactions, agents receive feedback from the environment and thus progressively adjust their behaviour to accomplish a given goal. An important question in reinforcement learning is how fast agents can learn to fulfill their tasks. To answer this question we consider a novel reinforcement learning framework where quantum mechanics is used. In particular, we quantize the agent and the environment and grant them the possibility to also interact quantum-mechanically, that is, by using a quantum channel for their communication. We demonstrate that this feature enables a speed-up in the agent's learning process, and we further show that combining this scenario with classical communication enables the evaluation of such an improvement. This learning protocol is implemented on an integrated re-programmable photonic platform interfaced with photons at telecommunication wavelengths. Thanks to the full tunability of the device, this platform proves the best candidate for the implementation of learning protocols, where a continuous update of the learning process is required.

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Valeria Saggio, Beate E. Asenbeck, Arne Hamann, Teodor Strömberg, Peter Schiansky, Vedran Dunjko, Nicolai Friis, Nicholas C. Harris, Michael Hochberg, Dirk Englund, Sabine Wölk, Hans J. Briegel, and Philip Walther "Quantum speed-ups in reinforcement learning", Proc. SPIE 11806, Quantum Nanophotonic Materials, Devices, and Systems 2021, 118060N (1 August 2021); https://doi.org/10.1117/12.2593720

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