Titlebook: Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context; Leonhard Kunczik Book 2022 The Editor(s) (if applicable) and

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Approximation in Quantum Computing,This chapter focuses on function approximation in quantum computing and introduces quantum variational circuits as a quantum approximator. The idea of hybrid training, which combines classical machine learning algorithms with quantum variational circuits is explained, to build the foundation for the new quantum Reinforcement Learning method.

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Future Steps in Quantum Reinforcement Learning for Complex Scenarios,This chapter summarizes future steps to enhance the performance of the quantum REINFORCE algorithm. It further shares some practical issues that arose while working with the IBM quantum hardware, which can be considered while developing other quantum policy gradient algorithms.

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forcement 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 comput

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,Motivation: Complex Attacker-Defender Scenarios—The Eternal Conflict,ems. The connection between Reinforcement Learning and quantum computing is drawn to reduce the required computational power in Reinforcement Learning and an outlook on the research questions is given.

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Applying Quantum REINFORCE to the Information Game,ring the results to the classical Q-learning and REINFORCE algorithms. The advantages of the new algorithm are derived and discussed. Additionally, details on the hyper-parameter optimization within the experiments are given.

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Book 2022Learning 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 po

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