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9 Quantum reinforcement learning: decision-making in quantum environments

Ashutosh Pagrotra, Vedant Dhiman

Published Jul 22, 2024• 0 citations
Publisher

Abstract

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.

DOI
10.1515/9783111342276-009

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The current natural evolution of the world of real-time decision-making algorithms is unable to keep up with the fast changing complexity and dynamics of today’s world. Particularly in automotive, smart grids, robotics, or other applying fields, such ultra efficient decision making is demanded in a situation of rapidly changing conditions with high requirements on computation resources. As the first contribution, Quantum Inspired Reinforcement Learning (QIRLF) proposes a new method of addressing these challenges via marrying the fundamental elements of quantum theory to reinforcement learning. At the same time, QIRLF exploits quantum inspired state representation to explore many potential states concomitantly, thereby improving the efficiency in its decisions making process. Besides quantum tunneling and probabilistic action choice mechanisms faster convergence to optimal solutions are possible in values of state spaces, and multi-agent entanglement improves cooperation between the agents. The framework is adaptable in being able to make real time decisions in dynamic environments and is efficient by having quantum feedback loops that continuously refine decision policies. QIRLF reduces significantly the decision-making time and computational load compared to traditional, by using quantum inspired techniques. By applying this approach we not only enhance decision speed and reliability but also help in developing effective, energy efficient solutions for the complex real time systems, making it possible for more advanced, environment friendly technologies in systems needing fast, accurate and quick decisions.