9 Quantum reinforcement learning: decision-making in quantum environments
Ashutosh Pagrotra, Vedant Dhiman · 2024
This chapter unites quantum mechanics with decision-making through an exciting journey through quantum reinforcement learning (QRL). It addresses fundamental RL principles and issues by severing the links between QRL and conventional reinforcement learning. The groundwork for quantum robotics and lights, where quantum organisms explore quantum environments and discover new frontiers, is laid via a quantum mechanics primer. By combining quantum annealing, search methods, and parallelism, QRL provides a novel remedy for the drawbacks of RL. It goes beyond traditional simulations and explores uncharted areas such as quantum chemistry and innovative financial techniques. Through education and remote quantum resources made possible by cloud services, QRL democratizes quantum solutions, increasing access and motivating future generations. It ignites curiosity by providing an amazing environment for learning and multidisciplinary study. Beyond computation, QRL inspires the next generation, advances science, and catalyzes fields.