Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context

Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context
Author: Leonhard Kunczik
Publisher ‏ : ‎ Springer Vieweg; 1st ed. 2022 edition (1 Jun. 2022)
Language ‏ : ‎ English
Paperback ‏ : ‎ 152 pages
ISBN-10 ‏ : ‎ 3658376155
ISBN-13 ‏ : ‎ 9783658376154

Book Description
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 Author: 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 Author: an in-depth analysis of the advantages of Quantum Reinforcement Learning.

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