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：
2014010
负责人：
Mark Wilde
金额：
$ 30万
依托单位：
Louisiana State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014010&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.

拟议研究的主要目标是开发理论工具，以评估连续变化的量子逻辑门，操作和渠道的性能。连续变化的量子计算是全球领先的学术团体正在追求的量子信息处理的有前途的量子计算方法，现在甚至有商业努力。量子计算有可能以深刻的方式改变世界，通过打破广泛使用的加密方法，从而更快地模拟量子化学反应（从而在医学中起着重要作用），并且最近发现，在优化和机器学习方面有可能加快速度。考虑到这些应用，必须通过量化这些量子计算机的性能来了解连续变化的方法。同样很明显，这项研究和相关应用程序为NSF的国家利益和使命服务：“促进国家健康，繁荣和福利；确保国防。”此外，这项研究对路易斯安那州 /密西西比河三角洲地区的影响更大。这项研究将增加LSU在扩展量子信息科学领域的研究生培训。研究生将在被子日公共论坛（路易斯安那州的量子信息）节中介绍结果，这是每个学期举行的为期一天的会议，它在路易斯安那州和附近的地区及其在量子信息中的最新进展和正在进行的研究中，该研究的技术贡献是通过该研究的技术贡献来量化的，该研究的实现了，该研究的技术贡献是通过可进行的一项可行的工具实现了多大的实施。连续变化的量子门是带有光的连续量子变量的量子计算的重要组成部分。许多量子信息处理架构中存在连续变量系统，包括超导，离子陷阱和光子设备。设计方法来量化连续变量量子设备的性能。尽管在离散可变量子信息处理中，该域在该域中取得了一些进展，但对于连续变化的情况，几乎没有可用的工具。该项目的主要目的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的性能的工具。特定的例子包括改善连续可变的传送协议以更有效地模拟理想的渠道，以及开发新颖的机器学习技术，以发现和改进该协议。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛的影响审查标准来通过评估来通过评估来获得支持的。