Quantum Policy

Quantum policy, a subfield of quantum machine learning, aims to leverage quantum computing to enhance reinforcement learning algorithms, ultimately improving the efficiency and scalability of solving complex decision-making problems. Current research focuses on developing quantum algorithms for policy iteration, employing techniques like variational quantum circuits and amplitude estimation to accelerate policy evaluation and improvement, often within the framework of mean-field games. These advancements hold the potential to significantly reduce the computational cost of reinforcement learning, impacting fields requiring optimal control in large-scale systems, such as robotics and resource management.