近日，中国工程物理研究院研究生院的傅立斌及其研究团队取得一项新进展。经过不懈努力，他们对各向异性中心自旋模型产生的自旋压缩进行研究。相关研究成果已于2024年5月13日在国际知名学术期刊《物理评论A》上发表。
本文研究了各向异性中心自旋系统中的自旋压缩和量子相变。研究人员发现这种中心自旋系统可以映射到各向异性的Lipkin-Meshkov-Glick模型，在该模型中，中心自旋与自旋浴之间的跃迁频率之比趋于无穷大。这一特性不仅引发了单轴扭曲相互作用，还为生成自旋压缩态提供了新的可能性。在研究中，研究人员分别探讨了通过基态和中心自旋模型的动力学演化两种方法来产生自旋压缩态。
结果表明，动力学方法更为高效，并且自旋压缩参数随着各向异性参数的减小而增大，同时随着系统尺寸的增大而增大，呈现出N-2/3的依赖关系。进一步，通过数值模拟，研究人员得到了量子费雪信息在临界点附近的临界指数，并观察到当频率比和系统尺寸均趋于无穷大时，该指数趋于4/3。本文提供了一种产生自旋压缩态的有前途的方案，为量子传感的潜在进展铺平了道路。
据悉，自旋压缩作为一种重要的量子资源，在量子计量中起着举足轻重的作用，使人们能够实现高精度的参数估计方案。
附：英文原文
Title: Spin squeezing generated by the anisotropic central spin model
Author: Lei Shao, Libin Fu
Issue&Volume: 2024/05/13
Abstract: Spin squeezing, as a crucial quantum resource, plays a pivotal role in quantum metrology, enabling us to achieve high-precision parameter estimation schemes. Here, we investigate the spin squeezing and the quantum phase transition in an anisotropic central spin system. We find that this kind of central spin system can be mapped to the anisotropic Lipkin-Meshkov-Glick model in the limit where the ratio of transition frequencies between the central spin and the spin bath tends towards infinity. This property can induce a one-axis twisting interaction and provides another possibility for generating spin squeezing. We consider the generation of spin-squeezed states through the ground state and the dynamical evolution of the central spin model, respectively. The results show that the dynamical approach is more effective, and the spin-squeezing parameter improves as the anisotropy parameter decreases, while its value scales with system size as N-2/3. Furthermore, we obtain the critical exponent of the quantum Fisher information around the critical point by numerical simulation, and find its value tends to 4/3 as the frequency ratio and the system size approach infinity. This paper offers a promising scheme for generating spin-squeezed state and paves the way for potential advancements in quantum sensing.
DOI: 10.1103/PhysRevA.109.052618
Source: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.109.052618
来源：科学网 小柯机器人