Reinforcement Learning with Hybrid Quantum Approximation in the NISQ Context

About the authorLeonhard Kunczik obtained his Dr. rer. nat. in 2021 in Quantum Reinforcement Learning from the Universität der Bundeswehr München as a member of the COMTESSA research group. Now, he continues his research as a project leader at the forefront of Quantum Machine Learning and Optimization in the context of Operations Research and Cyber Security.

• Motivation: Complex Attacker-Defender Scenarios - The eternal conflict., 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.