Warm-Start Variational Quantum Policy Iteration
<|reference_start|>Warm-Start Variational Quantum Policy Iteration: 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.<|reference_end|>
@article{meyer2024warm-start,
    title={Warm-Start Variational Quantum Policy Iteration},
    author={Nico Meyer, Jakob Murauer, Alexander Popov, Christian Ufrecht, Axel
  Plinge, Christopher Mutschler, and Daniel D. Scherer},
    journal={arXiv preprint arXiv:2404.10546},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10546},
    primaryClass={quant-ph cs.LG}
}