Reinforcement learning with hybrid quantum approximation in the NISQ context / Leonhard Kunczik.

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Reinforcement learning with hybrid quantum approximation in the NISQ context / Leonhard Kunczik.

9783658376161 (electronic bk.)
3658376163 (electronic bk.)
9783658376154
3658376155

Wiesbaden : Springer Vieweg, [2022]

©2022

English

1 online resource : illustrations

10.1007/978-3-658-37616-1 doi

Q325.6

006.3/1

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 author Leonhard Kunczik obtained his Dr. rer. nat. in 2021 in Quantum Reinforcement Learning from the Universitat der Bundeswehr Munchen 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.

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Online resource; title from PDF title page (SpringerLink, viewed June 14, 2022).

Print version: 9783658376154

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