论文标题
带自旋网络的最佳温度计
Optimal Thermometers with Spin Networks
论文作者
论文摘要
给定探针的热容量$ \ MATHCAL {C} $是一个基本数量，在其他属性中，温度估计中的最高精度。反过来，$ \ MATHCAL {C} $受二次缩放的限制，并具有探针组成数量的数量，该探针的数量提供了量子温度计的基本限制。通过逼真的探针（即实验性地适合）实现这一基本界限仍然是一个开放的问题。在这项工作中，我们通过使用旋转网络来解决工程最佳温度计的问题。将自己限制在两体相互作用中，我们得出了最佳配置的一般特性，并利用机器学习技术来找到最佳耦合。这导致了简单的体系结构，我们在分析上显示，近似于$ \ Mathcal {C} $的理论最大值，并维护短期和长期交互的最佳缩放。我们的模型可以在当前可用的量子退火器中进行编码，并在需要哈密顿工程的其他任务中找到应用，从量子加热发动机到绝热Grover的搜索。
The heat capacity $\mathcal{C}$ of a given probe is a fundamental quantity that determines, among other properties, the maximum precision in temperature estimation. In turn, $\mathcal{C}$ is limited by a quadratic scaling with the number of constituents of the probe, which provides a fundamental limit in quantum thermometry. Achieving this fundamental bound with realistic probes, i.e. experimentally amenable, remains an open problem. In this work, we tackle the problem of engineering optimal thermometers by using networks of spins. Restricting ourselves to two-body interactions, we derive general properties of the optimal configurations and exploit machine-learning techniques to find the optimal couplings. This leads to simple architectures, which we show analytically to approximate the theoretical maximal value of $\mathcal{C}$ and maintain the optimal scaling for short- and long-range interactions. Our models can be encoded in currently available quantum annealers, and find application in other tasks requiring Hamiltonian engineering, ranging from quantum heat engines to adiabatic Grover's search.
