Quantum phase estimation is a paradigmatic problem in quantum sensing and metrology. Here we show that adaptive methods based on classical machine learning algorithms can be used to enhance the precision of quantum phase estimation when noisy non-entangled qubits are used as sensors. We employ the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms to this task and we identify the optimal feedback policies which minimize the Holevo variance. We benchmark these schemes with respect to scenarios that include Gaussian and Random Telegraph fluctuations as well as reduced Ramsey-fringe visibility due to decoherence. We discuss their robustness against noise in connection with real experimental setups such as Mach–Zehnder interferometry with optical photons and Ramsey interferometry in trapped ions, superconducting qubits and nitrogen-vacancy (NV) centers in diamond.

中文翻译：

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用于具有噪声的中等规模量子传感器的自适应量子相位估计的基准机器学习算法

量子相位估计是量子传感和计量学中的一个典型问题。在这里，我们展示了当噪声非纠缠量子位用作传感器时，基于经典机器学习算法的自适应方法可用于提高量子相位估计的精度。我们将差分进化 (DE) 和粒子群优化 (PSO) 算法用于此任务，并确定了最小化 Holevo 方差的最佳反馈策略。我们根据包括高斯和随机电报波动以及由于退相干导致的拉姆齐边缘可见性降低的场景对这些方案进行基准测试。我们讨论了它们对噪声的鲁棒性与真实的实验设置有关，例如使用光学光子的 Mach-Zehnder 干涉测量和捕获离子中的 Ramsey 干涉测量，