Quantum reinforcement learning: Comparing quantum annealing and gate-based quantum computing with classical deep reinforcement learning

In this paper, we present implementations of an annealing-based and a gate-based quantum computing approach for finding the optimal policy to traverse a grid and compare them to a classical deep reinforcement learning approach. We extended these three approaches by allowing for stochastic actions in...

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Bibliographic Details
Published in:Quantum information processing 2023-02, Vol.22 (2), Article 125
Main Authors: Neumann, Niels M. P., de Heer, Paolo B. U. L., Phillipson, Frank
Format: Article
Language:English
Subjects:
Data Structures and Information Theory
Mathematical Physics
Physics
Physics and Astronomy
Quantum Computing
Quantum Information Technology
Quantum Physics
Spintronics
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Summary:In this paper, we present implementations of an annealing-based and a gate-based quantum computing approach for finding the optimal policy to traverse a grid and compare them to a classical deep reinforcement learning approach. We extended these three approaches by allowing for stochastic actions instead of deterministic actions and by introducing a new learning technique called curriculum learning. With curriculum learning, we gradually increase the complexity of the environment and we find that it has a positive effect on the expected reward of a traversal. We see that the number of training steps needed for the two quantum approaches is lower than that needed for the classical approach.
ISSN:1573-1332
1573-1332
DOI:10.1007/s11128-023-03867-9