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Quantum Reinforcement Learning: Advancing AI Agents Through Quantum Computing

Ved Seetohul, Hamid Jahankhani, Stefan Kendzierskyj, Isuru Sandakelum Will Arachchige

Published Jan 1, 2024• 10 citations
Publisher
DOI
10.1007/978-3-031-64045-2_4

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Autonomous agents are being used in more complex and dynamic environments where efficient learning, adaptive decision-making, and scalable optimization are required. Although classical machine learning (ML) and reinforcement methods have greatly contributed to agent intelligence, they are usually restricted by constraints of computational complexity, scaling, and convergence to high-dimensional decision space. The current developments in quantum computing have created new possibilities to solve these problems using quantum ML (QML). In this chapter, the authors discuss the implementation of QML in collaboration with autonomous agents in the larger paradigm of agentic artificial intelligence (AI) and quantum AI agents. It offers a hybrid classical–quantum architectural model where quantum learning modules improve perception, policy learning, and decision optimization, and do not conflict with classical agent pipelines. The chapter analyzes application cases in autonomous robotics, intelligent transportation systems, multi-agent coordination, smart energy management, as well as communication networks, and the chapter shows the potential benefits of quantum-enhanced learning in these areas. The issue of quantum hardware neediness, data encoding, scaling, hybrid integration, and real-life application are critically evaluated and unanswered. In summary, the chapter is a well-organized approach to the purpose of QML in the development of the next-generation autonomous agent and an insight into creating practical, scalable, and intelligent quantum AI agents.
Agentic artificial intelligence (AI) is a type of AI in which machines act like intelligent agents. These agents can observe their surroundings, learn from experience, think, and make decisions to achieve a goal. As AI agents are used in more complex tasks, normal computers often find it difficult to process large amounts of information quickly. Quantum computing is a new kind of computing that uses special rules of physics to solve certain problems faster than traditional computers. This chapter explains how quantum computing can act as a catalyst, or a powerful helper, for improving agentic AI. It first introduces the basic ideas of agentic AI and AI agents and then explains the key concepts of quantum computing in a simple way. The chapter shows how quantum computing can help AI agents learn faster, make better decisions, and find better solutions. It also discusses real-world applications, current limitations of quantum technology, and future possibilities. Overall, this chapter highlights how quantum computing can play an important role in building smarter and more capable AI agents.