论文标题
基于张量网络的机器学习非马克维亚量子流程
Tensor network based machine learning of non-Markovian quantum processes
论文作者
论文摘要
我们展示了如何使用基于张量的机器学习算法来学习通用，非马克维亚，量子随机过程的结构。我们通过将过程表示为矩阵产品运营商（MPO）来做到这一点，并在不同时间和相应的时间非局部输出状态使用本地输入状态的数据库进行训练。特别是，我们分析了一个与环境结合的量子，并在不同时间预测系统的输出状态，并重建完整的系统过程。我们展示了MPO的键维是一种非马克维亚性的度量，取决于系统的特性，环境及其相互作用。因此，这项研究为对过程张量及其特性的可能实验研究开辟了道路。
We show how to learn structures of generic, non-Markovian, quantum stochastic processes using a tensor network based machine learning algorithm. We do this by representing the process as a matrix product operator (MPO) and train it with a database of local input states at different times and the corresponding time-nonlocal output state. In particular, we analyze a qubit coupled to an environment and predict output state of the system at different time, as well as reconstruct the full system process. We show how the bond dimension of the MPO, a measure of non-Markovianity, depends on the properties of the system, of the environment and of their interaction. Hence, this study opens the way to a possible experimental investigation into the process tensor and its properties.
