A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning
arXiv:2309.16972 · doi:10.1142/s2010324723500133
Abstract
Due to the large state space of the two-qubit system, and the adoption of ladder reward function in the existing quantum state preparation methods, the convergence speed is slow and it is difficult to prepare the desired target quantum state with high fidelity under limited conditions. To solve the above problems, a difference-driven reinforcement learning (RL) algorithm for quantum state preparation of two-qubit system is proposed by improving the reward function and action selection strategy. Firstly, a model is constructed for the problem of preparing quantum states of a two-qubit system, with restrictions on the type of quantum gates and the time for quantum state evolution. In the preparation process, a weighted differential dynamic reward function is designed to assist the algorithm quickly obtain the maximum expected cumulative reward. Then, an adaptive e-greedy action selection strategy is adopted to achieve a balance between exploration and utilization to a certain extent, thereby improving the fidelity of the final quantum state. The simulation results show that the proposed algorithm can prepare quantum state with high fidelity under limited conditions. Compared with other algorithms, it has different degrees of improvement in convergence speed and fidelity of the final quantum state.
arXiv comment12 pages, 9 figures
References in corpus (15)
- Quantum control theory and applications: A survey
- Large-Scale Study of Curiosity-Driven Learning
- Soundness and completeness of quantum root-mean-square errors
- Fidelity-based Probabilistic Q-learning for Control of Quantum Systems
- Self-guided quantum tomography
- Deep Reinforcement Learning for Quantum Gate Control
- Optimal quantum control of Bose-Einstein condensates in magnetic microtraps: Comparison of GRAPE and Krotov optimization schemes
- Automatic spin-chain learning to explore the quantum speed limit
- Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
- Reconstruction of a Photonic Qubit State with Reinforcement Learning
- A Reinforcement Learning approach for Quantum State Engineering
- Classifying global state preparation via deep reinforcement learning
- Multiqubit and multilevel quantum reinforcement learning with quantum technologies
- Deep reinforcement learning for universal quantum state preparation via dynamic pulse control
- A quantum system control method based on enhanced reinforcement learning