Quantum Reinforcement Learning

Quantum reinforcement learning (QRL) aims to leverage quantum computing principles to enhance the efficiency and performance of reinforcement learning algorithms, particularly in tackling complex sequential decision-making problems. Current research focuses on developing hybrid quantum-classical models, often employing variational quantum circuits within established RL frameworks like deep Q-networks and actor-critic methods, and exploring the use of quantum-inspired algorithms like simulated annealing for optimization. This burgeoning field holds significant promise for accelerating the training of RL agents and potentially achieving superior performance in applications ranging from drug discovery and financial market prediction to robotics and quantum control, although the extent of practical quantum advantage remains an active area of investigation.