1. Warm-Start Variational Quantum Policy Iteration
- Author
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Meyer, Nico, Murauer, Jakob, Popov, Alexander, Ufrecht, Christian, Plinge, Axel, Mutschler, Christopher, and Scherer, Daniel D.
- Subjects
Quantum Physics ,Computer Science - Machine Learning - 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., Comment: Accepted to the IEEE International Conference on Quantum Computing and Engineering (QCE 2024), Montr\'eal, Qu\'ebec, Canada. 9 pages, 6 figures, 1 table
- Published
- 2024