Experimental quantum speed-up in reinforcement learning agents

• URL: http://arxiv.org/abs/2103.06294v1
• Date: Wed, 10 Mar 2021 19:01:12 GMT
• Title: Experimental quantum speed-up in reinforcement learning agents
• Authors: Valeria Saggio, Beate E. Asenbeck, Arne Hamann, Teodor Str\"omberg, Peter Schiansky, Vedran Dunjko, Nicolai Friis, Nicholas C. Harris, Michael Hochberg, Dirk Englund, Sabine W\"olk, Hans J. Briegel and Philip Walther
• Abstract summary: reinforcement learning (RL) is an important paradigm within artificial intelligence (AI) We present a RL experiment where the learning of an agent is boosted by utilizing a quantum communication channel with the environment. We implement this learning protocol on a compact and fully tunable integrated nanophotonic processor.
• Score: 0.17849902073068336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Increasing demand for algorithms that can learn quickly and efficiently has led to a surge of development within the field of artificial intelligence (AI). An important paradigm within AI is reinforcement learning (RL), where agents interact with environments by exchanging signals via a communication channel. Agents can learn by updating their behaviour based on obtained feedback. The crucial question for practical applications is how fast agents can learn to respond correctly. An essential figure of merit is therefore the learning time. While various works have made use of quantum mechanics to speed up the agent's decision-making process, a reduction in learning time has not been demonstrated yet. Here we present a RL experiment where the learning of an agent is boosted by utilizing a quantum communication channel with the environment. We further show that the combination with classical communication enables the evaluation of such an improvement, and additionally allows for optimal control of the learning progress. This novel scenario is therefore demonstrated by considering hybrid agents, that alternate between rounds of quantum and classical communication. We implement this learning protocol on a compact and fully tunable integrated nanophotonic processor. The device interfaces with telecom-wavelength photons and features a fast active feedback mechanism, allowing us to demonstrate the agent's systematic quantum advantage in a setup that could be readily integrated within future large-scale quantum communication networks.

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