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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.

该研究的主要目标是开发用于评估连续可变量子逻辑门、操作和通道性能的理论工具。连续变量量子计算是一种很有前途的量子信息处理方法，世界各地领先的学术团体正在研究这种方法，现在甚至已经有了商业化的努力。量子计算有可能以深刻的方式改变世界，它可以打破广泛使用的加密方法，可以更快地模拟量子化学反应（从而在医学中发挥重要作用），而且最近人们发现它有可能加速优化和机器学习。考虑到这些应用，有必要通过量化这些量子计算机的性能来理解连续变量方法。同样明确的是，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：“促进国民健康、繁荣和福利；确保国防安全。”此外，这项研究对路易斯安那州/密西西比河三角洲地区也有更广泛的影响。该研究将加强对路易斯安那州立大学研究生在不断扩大的量子信息科学领域的培训。研究生将在 QuILT Day（路易斯安那州量子信息日）的公共论坛上展示研究结果，该会议每学期举行一次为期一天的会议，路易斯安那州及附近地区的多个研究小组聚集在一起讨论量子信息方面的最新进展和正在进行的研究。该研究的技术贡献是通过连续变量操作的实验近似与其理想实现的偏差程度来精确量化。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架构中，包括超导、离子陷阱和光子器件。设计量化连续可变量子器件性能的方法至关重要。尽管在离散变量量子信息处理领域已经取得了一些进展，但可用于连续变量情况的工具却并不多。该项目的主要目标之一是纠正这种情况，其想法是开发用于量化连续可变量子门和操作性能的工具。具体的例子包括改进连续变量隐形传输协议以更有效地模拟理想通道，以及开发新颖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价值和更广泛的影响审查标准进行评估，被认为值得支持。