Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context
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Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context
Kunczik, Leonhard
Springer Fachmedien Wiesbaden
06/2022
134
Mole
Inglês
9783658376154
15 a 20 dias
211
Descrição não disponível.
Motivation: Complex Attacker-Defender Scenarios - The eternal con?ict., The Information Game - A special Attacker-Defender Scenario., Reinforcement Learning and Bellman's Principle of Optimality., Quantum Reinforcement Learning - Connecting Reinforcement Learning and Quantum Computing.- Approximation in Quantum Computing.- Advanced Quantum Policy Approximation in Policy Gradient Rein-forcement Learning.- Applying Quantum REINFORCE to the Information Game.- Evaluating quantum REINFORCE on IBM's Quantum Hardware.- Future Steps in Quantum Reinforcement Learning for Complex Scenarios.- Conclusion.
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Quantum Machine Learning;Quantum Reinforcement Learning;Quanten Computing;Reinforcement Learning;Attacker-Defender Scenarios
Motivation: Complex Attacker-Defender Scenarios - The eternal con?ict., The Information Game - A special Attacker-Defender Scenario., Reinforcement Learning and Bellman's Principle of Optimality., Quantum Reinforcement Learning - Connecting Reinforcement Learning and Quantum Computing.- Approximation in Quantum Computing.- Advanced Quantum Policy Approximation in Policy Gradient Rein-forcement Learning.- Applying Quantum REINFORCE to the Information Game.- Evaluating quantum REINFORCE on IBM's Quantum Hardware.- Future Steps in Quantum Reinforcement Learning for Complex Scenarios.- Conclusion.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
