Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context

Leonhard Kunczik

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English

Springer Vieweg
01 June 2022

Computer security; Computer fraud & hacking; Network security; Machine learning

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.

By: Leonhard Kunczik
Imprint: Springer Vieweg
Country of Publication: Germany
Edition: 1st ed. 2022
Dimensions: Height: 210mm, Width: 148mm,
Weight: 211g
ISBN: 9783658376154
ISBN 10: 3658376155
Pages: 134
Publication Date: 01 June 2022
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.