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Quantifying and Optimizing the Performance of Continuous-Variable Quantum Logic Operations

量化和优化连续可变量子逻辑运算的性能

基本信息

批准号：
2304816
负责人：
Mark Wilde
金额：
$ 30万
依托单位：
Cornell University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-11-15 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304816&HistoricalAwards=false
关键词：
Quantifying Optimizing Performance Continuous Variable

项目摘要

The main goal of the proposed research is to develop theoretical tools for assessing the performance of continuous-variable quantum logic gates, operations, and channels. Continuous-variable quantum computing is a promising approach to quantum information processing that is being pursued by leading academic groups around the world, and there are now even commercial efforts. Quantum computing has the potential to change the world in profound ways, by allowing for breaking encryption methods that are in wide use, allowing for faster simulation of quantum chemical reactions (thus playing an important role in medicine), and more recently it has been discovered that there is the potential for speed-up in optimization and machine learning. With these applications in mind, it is essential to understand the continuous-variable approach, by quantifying the performance of these quantum computers. It is also clear that this research and the related applications serves the national interest and mission of the NSF: "to advance the national health, prosperity and welfare; to secure the national defense." Additionally, there are broader impacts of this research for the Louisiana / Mississippi River Delta Region. The research will increase the training of graduate students at LSU in the expanding field of quantum information science. Graduate students will present results in the public forum of QuILT Day (Quantum Information in Louisiana) Day, which is a day-long conference held each semester that brings several research groups throughout Louisiana and nearby regions to discuss recent advances and ongoing research in quantum information.The technical contribution of the research is to quantify precisely by how much an experimental approximation of a continuous-variable operation deviates from its ideal implementation. Continuous-variable quantum gates are an essential component of quantum computing with the continuous quantum variables of light. Continuous-variable systems are present in many quantum information processing architectures, including superconducting, ion trap, and photonic devices. It is essential to devise methods for quantifying the performance of continuous-variable quantum devices. While there have been several advances in this domain in discrete-variable quantum information processing, there are not nearly as many tools available for the continuous-variable case. One of the main aims of this project is to rectify this situation, with the idea being to develop tools for quantifying the performance of continuous-variable quantum gates and operations. Particular examples include improving continuous-variable teleportation protocols to more effectively simulate an ideal channel, as well as developing novel machine learning techniques in order to discover and improve such protocols.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究的主要目标是开发用于评估连续变量量子逻辑门、操作和通道的性能的理论工具。连续变量量子计算是一种很有前途的量子信息处理方法，世界各地的领先学术团体都在追求这种方法，现在甚至还有商业努力。量子计算有可能以深刻的方式改变世界，它可以破解广泛使用的加密方法，允许更快地模拟量子化学反应(从而在医学上发挥重要作用)，最近发现在优化和机器学习方面存在加速的潜力。考虑到这些应用，通过量化这些量子计算机的性能，理解连续变量方法是至关重要的。很明显，这项研究和相关应用服务于国家利益和国家科学基金会的使命：“促进国家健康、繁荣和福利；保障国防安全。”此外，这项研究对路易斯安那州/密西西比河三角洲地区还有更广泛的影响。这项研究将增加路易斯安那州立大学在不断扩大的量子信息科学领域对研究生的培训。研究生将在被子日(路易斯安那州的量子信息)日的公共论坛上展示结果，这是每学期举行的为期一天的会议，路易斯安那州和附近地区的几个研究小组将讨论量子信息领域的最新进展和正在进行的研究。这项研究的技术贡献是通过连续变量运算的实验近似偏离其理想实现的程度来精确量化。连续变量量子门是利用光的连续量子变量进行量子计算的重要组成部分。连续变量系统存在于许多量子信息处理结构中，包括超导、离子陷阱和光子器件。设计出量化连续变量量子器件性能的方法是至关重要的。虽然在离散变量量子信息处理领域已经有了一些进展，但对于连续变量情况，几乎没有那么多可用的工具。该项目的主要目标之一是纠正这种情况，其想法是开发工具来量化连续变量量子门和操作的性能。具体的例子包括改进连续变量的隐形传态协议以更有效地模拟理想的信道，以及开发新的机器学习技术以发现和改进此类协议。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。