论文标题
通过贝叶斯优化来制备量子状态
Preparing Quantum States by Measurement-feedback Control with Bayesian Optimization
论文作者
论文摘要
量子状态的制备对于执行量子计算和量子模拟至关重要。在这项工作中，我们提出了一个通用框架，用于通过结合测量反馈控制过程（MFCP）和机器学习方法来准备多体系统的接地状态。使用贝叶斯优化（BO）策略，确定MFCP中确定测量和反馈操作员的效率。以一维的玻色 - 哈伯德模型为例，我们表明BO可以生成最佳参数，尽管受操作员的限制，该参数可以将系统驱动到低能状态，并且典型量子轨迹的可能性很高。
Preparation of quantum states is of vital importance for performing quantum computations and quantum simulations. In this work, we propose a general framework for preparing ground states of many-body systems by combining the measurement-feedback control process (MFCP) and the machine learning method. Using the Bayesian optimization (BO) strategy, the efficiency of determining the measurement and feedback operators in the MFCP is demonstrated. Taking the one dimensional Bose-Hubbard model as an example, we show that BO can generate optimal parameters, although constrained by the operator basis, which can drive the system to the low energy state with high probability in typical quantum trajectories.
