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Reinforcement learning with hybrid q...

Kunczik, Leonhard.

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Reinforcement learning with hybrid quantum approximation in the NISQ context

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reinforcement learning with hybrid quantum approximation in the NISQ context/ by Leonhard Kunczik.
作者:	Kunczik, Leonhard.
出版者:	Wiesbaden :Springer Fachmedien Wiesbaden : : 2022.,
面頁冊數:	xviii, 134 p. :ill., digital ;24 cm.
內容註:	Motivation: Complex Attacker-Defender Scenarios - The eternal conﬂict., The Information Game - A special Attacker-Defender Scenario., Reinforcement Learning and Bellman's Principle of Optimality., Quantum Reinforcement Learning - Connecting Reinforcement Learning and Quantum Computing -- Approximation in Quantum Computing -- Advanced Quantum Policy Approximation in Policy Gradient Rein-forcement Learning -- Applying Quantum REINFORCE to the Information Game -- Evaluating quantum REINFORCE on IBM's Quantum Hardware -- Future Steps in Quantum Reinforcement Learning for Complex Scenarios -- Conclusion.
Contained By:	Springer Nature eBook
標題:	Reinforcement learning. -
電子資源:	https://doi.org/10.1007/978-3-658-37616-1
ISBN:	9783658376161

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

Reinforcement learning with hybrid quantum approximation in the NISQ context[electronic resource] /by Leonhard Kunczik. - Wiesbaden :Springer Fachmedien Wiesbaden :2022. - xviii, 134 p. :ill., digital ;24 cm.

Motivation: Complex Attacker-Defender Scenarios - The eternal conﬂict., The Information Game - A special Attacker-Defender Scenario., Reinforcement Learning and Bellman's Principle of Optimality., Quantum Reinforcement Learning - Connecting Reinforcement Learning and Quantum Computing -- Approximation in Quantum Computing -- Advanced Quantum Policy Approximation in Policy Gradient Rein-forcement Learning -- Applying Quantum REINFORCE to the Information Game -- Evaluating quantum REINFORCE on IBM's Quantum Hardware -- Future Steps in Quantum Reinforcement Learning for Complex Scenarios -- Conclusion.

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.

ISBN: 9783658376161

Standard No.: 10.1007/978-3-658-37616-1doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6 / .K85 2022

Dewey Class. No.: 006.31

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520 $a 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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