Functional Analytic Methods in Matrix Theory, Majorization and Quantum Information

矩阵理论、大化和量子信息中的泛函分析方法

• 批准号: RGPIN-2022-04149
• 负责人: Pereira, Rajesh
• 金额: $ 1.53万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759093
• 关键词: Functional; Analytic; Methods; Matrix; Theory

This research project aims to use techniques from functional analysis to study and solve some interconnected problems in quantum computing, matrix theory and the majorization order.  Success in this research project will deepen our understanding of the interconnections between these fields and will contribute to developing some interesting mathematical techniques that are useful in quantum computation.  Rudimentary quantum computers are already being built and advances in our understanding of quantum computation can yield significant technological advancement.

该研究项目旨在利用泛函分析技术研究和解决量子计算中的一些相互关联的问题，矩阵理论和优序。这个研究项目的成功将加深我们对这些领域之间相互联系的理解，并将有助于开发一些有趣的数学技术，这些技术在量子计算中很有用。基本的量子计算机已经在建造和发展中我们对量子计算的理解可以带来重大的技术进步。