Quantum-Inspired Machine Learning

受量子启发的机器学习

• 批准号: DP200103760
• 负责人: A/Prof Ian McCulloch
• 金额: $ 10.39万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-06-09 至 2023-08-25
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP200103760
• 关键词: Quantum; Inspired; Machine; Learning

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.

该项目旨在开发基于密切对应的新的机器学习技术 神经网络用于深度学习，张量网络用于量子物理。张量网络是一种信息压缩形式，可用于机器学习，以比深层神经网络更易于理解内部结构的方式构建大数据集的紧凑表示。该项目的预期结果包括更具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。由此产生的好处包括增强了跨学科协作的能力，并改进了未来工业应用的方法。