Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context
1st ed. 2022
Book Details
Format
Paperback / Softback
ISBN-10
3658376155
ISBN-13
9783658376154
Edition
1st ed. 2022
Publisher
Springer Fachmedien Wiesbaden
Imprint
Springer Vieweg
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Jun 1st, 2022
Print length
134 Pages
Weight
218 grams
Dimensions
14.70 x 20.90 x 1.60 cms
Product Classification:
Computer securityComputer fraud & hackingNetwork securityMachine learning
Ksh 10,100.00
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This book explores the combination of Reinforcement Learning and Quantum Computing in the light of complex attacker-defender scenarios.
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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