Tensor networks for quantum simulation and machine learning

用于量子模拟和机器学习的张量网络

• 批准号: 2407107
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2407107
• 关键词: Tensor; networks; quantum; simulation; machine

Tensor networks are a method to describe quantum wavefunctions. They capture the important aspects of the wavefunctions so that the simulation of quantum systems is possible. Methods using tensor networks for 1D systems are some of the best performing classical methods of quantum simulation. These methods translate naturally to use on quantum computers, allowing the identication of a quantum advantage. Tensor networks have also been shown to be useful in classical machine learning algorithms. The translations of these algorithms to quantum computers may similarly provide an advantage. The insights gained from using tensor networks for quantum simulation may aid in the training of quantum machine learning circuits.

张量网络是一种描述量子波函数的方法。它们捕获了波函数的重要方面，从而使量子系统的模拟成为可能。使用张量网络进行一维系统的方法是量子模拟的一些最佳经典方法。这些方法自然地转化为在量子计算机上使用，允许识别量子优势。张量网络在经典机器学习算法中也被证明是有用的。这些算法到量子计算机的转换可以类似地提供优势。从使用张量网络进行量子模拟中获得的见解可能有助于训练量子机器学习电路。