Warm-Start Variational Quantum Policy Iteration

arXiv:2404.10546v1 Announce Type: cross
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 …

abstract algorithm arxiv behavior cs.lg decision framework iteration linear making nisq policy quant-ph quantum reinforcement reinforcement learning solve type warm