近日，复旦大学肖艳红团队研究了并行自旋压缩和场跟踪与机器学习。2025年4月8日出版的《自然—物理学》杂志发表了这项成果。

压缩和纠缠在量子计量方法中起着至关重要的作用。然而，在连续信号跟踪中证明量子增强仍然是一项具有挑战性的工作，因为同时产生纠缠和信号扰动通常是不兼容的。研究组证明，在恒定光泵浦下，使用连续量子非破坏测量可以同时实现稳态自旋压缩和传感。他们使用计量相关的稳态压缩实现了具有大量热原子系综的持续自旋压缩态。

研究组进一步利用该系统跟踪不同类型的连续时间波动磁场，并演示了使用深度学习模型从光学测量中推断时变磁场。在故意阻止自旋压缩的测试实验中，通过性能下降验证了自旋压缩引起的量子增强。这些结果代表了纠缠原子连续量子增强计量学的进步，包括训练和应用深度神经网络来推断复杂的时间依赖扰动。

附：英文原文

Title: Concurrent spin squeezing and field tracking with machine learning

Author: Duan, Junlei, Hu, Zhiwei, Lu, Xingda, Xiao, Liantuan, Jia, Suotang, Mlmer, Klaus, Xiao, Yanhong

Issue&Volume: 2025-04-08

Abstract: Squeezing and entanglement play crucial roles in approaches for quantum metrology. Yet, demonstrating quantum enhancement in continuous signal tracking remains a challenging endeavour because simultaneous entanglement generation and signal perturbations are often incompatible. We demonstrate that concurrent steady-state spin squeezing and sensing are possible using continuous quantum non-demolition measurements under constant optical pumping. We achieve a sustained spin-squeezed state with a large ensemble of hot atoms using metrologically relevant steady-state squeezing. We further employ the system to track different types of continuous time-fluctuating magnetic fields, and we demonstrate the use of deep learning models to infer the time-varying fields from an optical measurement. The quantum enhancement due to spin squeezing was verified by a degraded performance in test experiments where the spin squeezing was deliberately prevented. These results represent an advance in continuous quantum-enhanced metrology with entangled atoms, including the training and application of a deep neural network to infer complex time-dependent perturbations.

DOI: 10.1038/s41567-025-02855-3

Source: https://www.nature.com/articles/s41567-025-02855-3