Quantum-Inspired Machine Learning

该项目旨在开发新的机器学习技术，该技术基于以下内容之间的密切对应关系： 用于深度学习的神经网络和用于量子物理学的张量网络。张量网络是一种信息压缩形式，在机器学习中非常有用，可以以比深度神经网络更容易理解内部结构的方式构建大型数据集的紧凑表示。该项目的预期成果包括更有弹性的机器学习算法，以及表示将影响基础物理的量子态的新方法。由此带来的好处包括增强跨学科协作的能力，以及改进未来工业应用的方法。

This project aims to develop new machine learning techniques based around the close correspondence between neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this project include more resilient algorithms for machine learning, and new ways to represent quantum states that will impact fundamental physics. The resulting benefits include enhanced capacity for cross-discipline collaboration, and improved methods for future industrial applications.