Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context is popular PDF and ePub book, written by Leonhard Kunczik in 2022-05-31, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context can be Read Online from any device for your convenience.
Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context Book PDF Summary
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.
Detail Book of Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context PDF
- Author : Leonhard Kunczik
- Release : 31 May 2022
- Publisher : Springer Nature
- ISBN : 9783658376161
- Genre : Computers
- Total Page : 145 pages
- Language : English
- PDF File Size : 16,5 Mb
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