论文标题
使用深神经网络学习量子系统的潜力
Learning Potentials of Quantum Systems using Deep Neural Networks
论文作者
论文摘要
在最近的文献中，将神经网络（NN）应用于广泛的研究问题的尝试一直无处不在。特别是，使用深NN用于理解复杂的物理和化学现象已经打开了新的科学领域，其中机器学习中的分析工具（ML）与自然科学的计算概念相结合。来自ML统一的报告提供了证据，表明NNS可以学习古典的哈密顿力学。 NNS在古典物理学上的应用及其结果激发了以下问题：NN可以通过观察作为对量子现象提供见解的手段来赋予诱导偏见吗？在这项工作中，通过研究可能仅使用从系统的概率分布中获得的有限信息，以无监督的方式研究可能以无监督方式重建量子系统的哈密顿量来解决这个问题。
Attempts to apply Neural Networks (NN) to a wide range of research problems have been ubiquitous and plentiful in recent literature. Particularly, the use of deep NNs for understanding complex physical and chemical phenomena has opened a new niche of science where the analysis tools from Machine Learning (ML) are combined with the computational concepts of the natural sciences. Reports from this unification of ML have presented evidence that NNs can learn classical Hamiltonian mechanics. This application of NNs to classical physics and its results motivate the following question: Can NNs be endowed with inductive biases through observation as means to provide insights into quantum phenomena? In this work, this question is addressed by investigating possible approximations for reconstructing the Hamiltonian of a quantum system in an unsupervised manner by using only limited information obtained from the system's probability distribution.
