Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context (Paperback, 1st ed. 2022)

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.

General

Imprint:	Springer Vieweg
Country of origin:	Germany
Release date:	June 2022
First published:	2022
Authors:	Leonhard Kunczik
Dimensions:	210 x 148 x 16mm (L x W x T)
Format:	Paperback
Pages:	134
Edition:	1st ed. 2022
ISBN-13:	978-3-658-37615-4
Categories:	Books > Computing & IT > Computer communications & networking > Network security Books > Computing & IT > Social & legal aspects of computing > Computer fraud & hacking Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-658-37615-5
Barcode:	9783658376154

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