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Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context

Leonhard Kunczik (Autor)

Buch | Softcover

XVIII, 134 Seiten

2022 | 1st ed. 2022
Springer Fachmedien Wiesbaden GmbH (Verlag)
978-3-658-37615-4 (ISBN)

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This book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios. Reinforcement Learning has proven its capabilities in different challenging optimization problems and is now an established method in Operations Research. However, complex attacker-defender scenarios have several characteristics that challenge Reinforcement Learning algorithms, requiring enormous computational power to obtain the optimal solution. The upcoming field of Quantum Computing is a promising path for solving computationally complex problems. Therefore, this work explores a hybrid quantum approach to policy gradient methods in Reinforcement Learning. It proposes a novel quantum REINFORCE algorithm that enhances its classical counterpart by Quantum Variational Circuits. The new algorithm is compared to classical algorithms regarding the convergence speed and memory usage on several attacker-defender scenarios with increasing complexity. In addition, to study its applicability on today's NISQ hardware, the algorithm is evaluated on IBM's quantum computers, which is accompanied by an in-depth analysis of the advantages of Quantum Reinforcement Learning.

About the authorLeonhard Kunczik obtained his Dr. rer. nat. in 2021 in Quantum Reinforcement Learning from the Universität der Bundeswehr München as a member of the COMTESSA research group. Now, he continues his research as a project leader at the forefront of Quantum Machine Learning and Optimization in the context of Operations Research and Cyber Security.

Motivation: Complex Attacker-Defender Scenarios - The eternal con ict., The Information Game - A special Attacker-Defender Scenario., Reinforcement Learning and Bellman's Principle of Optimality., Quantum Reinforcement Learning - Connecting Reinforcement Learning and Quantum Computing.- Approximation in Quantum Computing.- Advanced Quantum Policy Approximation in Policy Gradient Rein-forcement Learning.- Applying Quantum REINFORCE to the Information Game.- Evaluating quantum REINFORCE on IBM's Quantum Hardware.- Future Steps in Quantum Reinforcement Learning for Complex Scenarios.- Conclusion.

Erscheinungsdatum	02.06.2022
Zusatzinfo	XVIII, 134 p. 38 illus.
Verlagsort	Wiesbaden
Sprache	englisch
Maße	148 x 210 mm
Gewicht	208 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	Attacker-Defender Scenarios • Quanten Computing • Quantum machine learning • Quantum Reinforcement Learning • Reinforcement Learning
ISBN-10	3-658-37615-5 / 3658376155
ISBN-13	978-3-658-37615-4 / 9783658376154
Zustand	Neuware