论文标题
量子过程的记忆复杂性
Memory Complexity of Quantum Processes
论文作者
论文摘要
通用的开放量子动力学可以通过两种看似非常不同的方法来描述:通过考虑(未知)环境与系统耦合并影响系统的观察到的动力学,一种自上而下的方法;或一种自下而上的方法,该方法试图从观察到的数据中构建开放的量子演变模型。该过程张量框架描述了人们可能在量子系统上进行的所有可能观察结果,但是它在计算效率低下且不预测。在这里,我们定义了纯化的过程张量,其中1)允许有效的层析成像以及未来过程的预测以及2)自然定义了固定的量子过程以及对记忆复杂性的定量且易于评估的定义,或者是非雄高的程度。因此,它允许发现隐藏在观察到的数据中的最小开放量子进化模型,从而完成了第二种方法以理解开放量子动力学。量子过程与经典随机过程之间的紧密联系是最终提出的。
Generic open quantum dynamics can be described by two seemingly very distinct approaches: a top down approach by considering an (unknown) environment coupled to the system and affects the observed dynamics of the system; or a bottom up approach which tries to build an open quantum evolution model from the observed data. The process tensor framework describes all the possible observations one could possibly make on a quantum system, however it is computationally inefficient and not predictive. Here we define the purified process tensor which 1) allows efficient tomography as well as prediction for future process and 2) naturally defines a stationary quantum process as well as a quantitative and easy-to-evaluate definition of the memory complexity, or the degree of non-Markovianity, for it. As such it allows to uncover the minimal open quantum evolution model hidden in the observed data, completing the second approach for understanding open quantum dynamics. The intimate connection between quantum processes and classical stochastic processes is drawn in the end.