论文标题
来自粗粒描述的量子状态推断：分析和量子热力学的应用
Quantum state inference from coarse-grained descriptions: analysis and an application to quantum thermodynamics
论文作者
论文摘要
物理系统的表征依赖于可观察到的属性，以及如何执行此类测量。在这里，我们分析了将描述分配给量子系统的两种方法，假设我们只能访问粗粒属性。更具体地说，我们将最大熵原理方法与贝叶斯启发的最近提出的平均分配图方法进行比较[P. S. Correia等人，物理。修订版A 103，052210（2021）]。尽管通过这两种方法的指定描述都尊重所测量的约束，并且它们共享相同的概念基础，但描述在超出传统系统 - 环境结构的情况下的描述也有所不同。因此，对于复杂量子系统的越来越普遍的情况，平均分配图被证明是更明智的选择。我们讨论了这种差异背后的物理学，并在量子热力学过程中进一步利用它。
The characterization of physical systems relies on the observable properties which are measured, and how such measurements are performed. Here we analyze two ways of assigning a description to a quantum system assuming that we only have access to coarse-grained properties. More specifically, we compare the Maximum Entropy Principle method, with the Bayesian-inspired recently proposed Average Assignment Map method [P. S. Correia et al, Phys. Rev. A 103, 052210 (2021)]. Despite the fact that the assigned descriptions by both methods respect the measured constraints, and that they share the same conceptual foundations, the descriptions differ in scenarios that go beyond the traditional system-environment structure. The Average Assignment Map is thus shown to be a more sensible choice for the ever more prevalent scenario of complex quantum systems. We discuss the physics behind such a difference, and further exploit it in a quantum thermodynamics process.
