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Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context

Af: Leonhard Kunczik Engelsk Paperback

Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context

Af: Leonhard Kunczik Engelsk Paperback
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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.

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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.

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Produktdetaljer
Sprog: Engelsk
Sider: 134
ISBN-13: 9783658376154
Indbinding: Paperback
Udgave:
ISBN-10: 3658376155
Kategori: Machine learning
Udg. Dato: 1 jun 2022
Længde: 16mm
Bredde: 209mm
Højde: 147mm
Forlag: Springer Fachmedien Wiesbaden
Oplagsdato: 1 jun 2022
Forfatter(e): Leonhard Kunczik
Forfatter(e) Leonhard Kunczik

Kategori Machine learning

ISBN-13 9783658376154

Sprog Engelsk

Indbinding Paperback

Sider 134

Udgave

Længde 16mm

Bredde 209mm

Højde 147mm

Udg. Dato 1 jun 2022

Oplagsdato 1 jun 2022

Forlag Springer Fachmedien Wiesbaden

Kategori sammenhænge