Modelling non-markovian quantum processes with recurrent neural networks
Modelling non-markovian quantum processes with recurrent neural networks
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DOI：
10.1088/1367-2630/aaf749
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发表时间：
2018-08
影响因子：
3.3
通讯作者：
L. Banchi;Edward Grant;Andrea Rocchetto;S. Severini
中科院分区：
文献类型：
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作者：
L. Banchi;Edward Grant;Andrea Rocchetto;S. Severini
与未知环境相互作用的量子系统非常难以建模，特别是在存在非马尔可夫和非微扰效应的情况下。在这里，我们介绍了一种基于神经网络的方法，它具有数学简单的Gorini-Kossakowski-Sudarshan-Lindblad主方程，但能够在不同的制度非马尔可夫效应建模。这是通过使用递归神经网络（RNN）来定义Lindblad算子来实现的，该算子可以跟踪记忆效应。在这个框架的基础上，我们还引入了一个神经网络架构，该架构能够在给定初始状态的情况下再现整个量子演化。作为应用，我们研究了如何训练这些模型用于量子过程层析成像，表明RNN在不同的时间和制度下都是准确的。
Quantum systems interacting with an unknown environment are notoriously difficult to model, especially in presence of non-Markovian and non-perturbative effects. Here we introduce a neural network based approach, which has the mathematical simplicity of the Gorini–Kossakowski–Sudarshan–Lindblad master equation, but is able to model non-Markovian effects in different regimes. This is achieved by using recurrent neural networks (RNNs) for defining Lindblad operators that can keep track of memory effects. Building upon this framework, we also introduce a neural network architecture that is able to reproduce the entire quantum evolution, given an initial state. As an application we study how to train these models for quantum process tomography, showing that RNNs are accurate over different times and regimes.