Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context
Author: Leonhard Kunczik
Publisher Finelybook 出版社: Vieweg; 1st ed. 2022 edition (1 Jun. 2022)
Language 语言: English
pages 页数: 152 pages
ISBN-10 书号: 3658376155
ISBN-13 书号: 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.