Deep Reinforcement Learning Archives - IEEE Transactions on Quantum Engineering

Fast State Stabilization Using Deep Reinforcement Learning for Measurement-Based Quantum Feedback Control

September 4, 2025Chunxiang Song Yanan Liu Daoyi Dong Hidehiro YonezawaQuantum ComputingConvergence, Current measurement, Deep Reinforcement Learning, Feedback control, Quantum computing, Quantum entanglement, Quantum state, Quantum system, Trajectory, Velocity measurement

Abstract: The stabilization of quantum states is a fundamental problem for realizing various quantum technologies. Measurement-based-feedback strategies have demonstrated powerful performance, and the construction of quantum control signals using measurement information has attracted great interest. However, the interaction between quantum systems and the environment is inevitable, especially when measurements are introduced, which leads to decoherence. […]

On Quantum Natural Policy Gradients

June 24, 2024André Sequeira, Luis Paulo Santos, Luis Soares Barbosa,Regular, UncategorizedBasic Conditions, Deep Reinforcement Learning, Estimation Error, Expected Value, Fisher Information, Fisher Information Matrix, Gradient Ascent, Gradient Update, Infidelity, Less Than Or Equal, Markov Decision Process, Matrix Inequalities, Natural Gradient, Normal Vector, Optimal Policy, Policy Gradient, Policy Gradient Method, Policy Parameters, Policy Space, Positive Semidefinite Matrix, Preconditioning, Projector, Proximal Policy Optimization, Quantum circuit, Quantum Measurement, Quantum state, Reward Function, Square Root, Standard Gradient, State Space

This article delves into the role of the quantum Fisher information matrix (FIM) in enhancing the performance of parameterized quantum circuit (PQC)-based reinforcement learning agents. While previous studies have highlighted the effectiveness of PQC-based policies preconditioned with the quantum FIM in contextual bandits, its impact in broader reinforcement learning contexts, such as Markov decision processes, […]