论文标题
量子计量学深入的强化学习有效而强大的纠缠产生
Efficient and Robust Entanglement Generation with Deep Reinforcement Learning for Quantum Metrology
论文作者
论文摘要
量子计量学利用量子资源和策略来提高未知参数的测量精度。一个至关重要的问题是如何准备适合高精度测量的量子纠缠状态,超过标准量子限制。在这里,我们提出了一个方案,以找到最佳的脉冲序列,以借助深钢筋学习(DRL)加速纠缠产生的单轴扭曲动力学。我们将脉冲序列视为沿一个轴或两个正交轴的$π/2 $脉冲的序列,并且通过使用DRL最大化量子Fisher信息来确定操作。在有限的演化时间内,准备好的纠缠状态的最终精度范围遵循了海森堡限制量表。这些状态也可以用作拉姆西干涉法的输入状态,最终测量精度仍然遵循海森堡限制量表。虽然仅沿着一个轴的脉冲序列更简单,更有效,但使用沿两个正交轴的脉冲序列的方案显示出更好的鲁棒性对原子数偏差。我们使用DRL的协议有效且易于在最新的实验中实现。
Quantum metrology exploits quantum resources and strategies to improve measurement precision of unknown parameters. One crucial issue is how to prepare a quantum entangled state suitable for high-precision measurement beyond the standard quantum limit. Here, we propose a scheme to find optimal pulse sequence to accelerate the one-axis twisting dynamics for entanglement generation with the aid of deep reinforcement learning (DRL). We consider the pulse train as a sequence of $π/2$ pulses along one axis or two orthogonal axes, and the operation is determined by maximizing the quantum Fisher information using DRL. Within a limited evolution time, the ultimate precision bounds of the prepared entangled states follow the Heisenberg-limited scalings. These states can also be used as the input states for Ramsey interferometry and the final measurement precisions still follow the Heisenberg-limited scalings. While the pulse train along only one axis is more simple and efficient, the scheme using pulse sequence along two orthogonal axes show better robustness against atom number deviation. Our protocol with DRL is efficient and easy to be implemented in state-of-the-art experiments.