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Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context - Tapa blanda

Kunczik, Leonhard

 
9783658376154: Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context
Tapa blanda
ISBN 10: 3658376155 ISBN 13: 9783658376154
Editorial: Springer Vieweg, 2022

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

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.

De la contraportada

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 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.

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Editorial
Springer Vieweg
Año de publicación
2022
Idioma
Inglés
ISBN 10 
3658376155
ISBN 13 
9783658376154
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
152
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