A Quantum System Control Method Based On Enhanced Reinforcement Learning

Abstract

Traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. To learn a satisfactory control strategy under the condition of limited resources, a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. The states and actions in reinforcement learning are mapped to quantum states and control operations in quantum systems. By using new enhanced neural networks, reinforcement learning can quickly achieve the maximization of long-term cumulative rewards, and a quantum state can be evolved accurately from an initial state to a target state. According to the number of candidate unitary operations, the three-switch control is used for simulation experiments. Compared with other methods, the QSC-ERL achieves close to 1 fidelity learn

Stats

• citations23
• S2 citations—
• github stars0
• HF likes0
• heat score10.35
• arxiv keyliu2023a

Related papers

• Curriculum-based Deep Reinforcement Learning For Quantum Control (2020)0.00
• Auxiliary Task-based Deep Reinforcement Learning For Quantum Control (2023)5.84
• Quantum Control Based On Deep Reinforcement Learning (2022)0.00
• Reinforcement Learning To Learn Quantum States For Heisenberg Scaling Accuracy (2024)0.00
• Efficient Quantum Recurrent Reinforcement Learning Via Quantum Reservoir Computing (2023)0.00
• An Introduction To Quantum Reinforcement Learning (QRL) (2024)0.00
• A Survey On Quantum Reinforcement Learning (2022)0.00
• On The Convergence Of Projective-simulation-based Reinforcement Learning In Markov Decision Processes (2019)8.35