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arXiv · 2404.10546

Warm-Start Variational Quantum Policy Iteration

Nico Meyer·Jakob Murauer·Alexander Popov·Christian Ufrecht·Axel Plinge·Christopher Mutschler·Daniel D. Scherer
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Abstract

Reinforcement learning is a powerful framework aiming to determine optimal behavior in highly complex decision-making scenarios. This objective can be achieved using policy iteration, which requires to solve a typically large linear system of equations. We propose the variational quantum policy iteration (VarQPI) algorithm, realizing this step with a NISQ-compatible quantum-enhanced subroutine. Its scalability is supported by an analysis of the structure of generic reinforcement learning environments, laying the foundation for potential quantum advantage with utility-scale quantum computers. Furthermore, we introduce the warm-start initialization variant (WS-VarQPI) that significantly reduces resource overhead. The algorithm solves a large FrozenLake environment with an underlying 256x256-dimensional linear system, indicating its practical robustness.

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Nico Meyer, Jakob Murauer, Alexander Popov, Christian Ufrecht, Axel Plinge, Christopher Mutschler, Daniel D. Scherer. 2024-04-16. Warm-Start Variational Quantum Policy Iteration. https://doi.org/10.1109/qce60285.2024.00172

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