Scalable Quantum Computing

可扩展的量子计算

• 批准号: RGPIN-2019-04840
• 负责人: Sanders, Barry
• 金额: $ 4.44万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714479
• 关键词: Scalable; Quantum; Computing

Quantum computing aims to solve some classical (i.e., non-quantum) problems faster than classical computers allow. Technically, this speedup means that the computational time scales better for a quantum computer than for a classical computer. Three examples help to explain. Whereas the time to find a marked item in an unstructured search of N items is linear in N classically, a quantum search is provably quadratically better, i.e., requiring only the square root of N evaluations. Quantum factorization, which is based on finding hidden periodicity efficiently on a quantum computer, is superpolynomially faster than the sub-exponential-time classical number-field sieve and has potential applications to linear-equation solvers. Quantum annealers aspire to demonstrate speedup over classical computing for some hard optimization problems and the quantum advantage is an open question. Optimization problems are ubiquitous, such as in finance and protein folding, so success with quantum annealing could have enormous positive ramifications for society. A key point of these goals is that the quantum computer itself must scale well to larger numbers of qubits and run-times or the theoretical advantage of quantum computing is lost to hardware problems. This research program addresses and seeks to resolve the problems of scalability, driven by the principle of co-design, namely that the type of problem being solved influences choices about architecture and design and the limitations of the computer influences which computational problems are suitable for quantum computing. This research involves one undergraduate student annually, five graduate students and one postdoctoral researcher in strategically important research topics that underpin positively disrupting, i.e., rapidly transforming, computer technology with ramifications in many areas of science, technology and beyond. These trainees will have opportunities to work with experts in experimental physics and in computer science, which prepares them for academic and industrial opportunities in quantum computing. This project aims to advance scalability of quantum computing along the following avenues, namely designing high-fidelity quantum circuits, developing quantum algorithms for dozens-of-qubits quantum computing, creating algorithms for sampling problems inspired by the BosonSampling problem, articulating verification-and-validation to know that components and devices meet standards and do what they are supposed to, and establishing confidentiality, integrity and availability for interconnected quantum things. In this last case, things refers to components, devices and systems and pertains to quantum networking, multi-party quantum communication and quantum computing on the cloud. One graduate student will work on each of these five avenues and a postdoctoral researcher will have the grand view and interconnect these discoveries. The undergraduate students will focus on solving technical issues.

量子计算旨在解决一些经典的（即，非量子）问题比经典计算机允许的更快。从技术上讲，这种加速意味着量子计算机的计算时间比经典计算机更好。有三个例子可以帮助解释。而在N个项目的非结构化搜索中找到标记项目的时间在经典上是N的线性，量子搜索可证明是二次方更好，即，仅需要N个求值的平方根。量子因式分解是基于在量子计算机上有效地找到隐藏的周期性，比亚指数时间经典数域筛快超多项式，并有潜在的应用线性方程求解器。量子退火器渴望证明在一些困难的优化问题上比经典计算加速，量子优势是一个悬而未决的问题。优化问题无处不在，例如在金融和蛋白质折叠中，因此量子退火的成功可能会对社会产生巨大的积极影响。 这些目标的一个关键点是，量子计算机本身必须能够很好地扩展到更大数量的量子位和运行时间，否则量子计算的理论优势就会因硬件问题而丧失。该研究计划旨在解决可扩展性问题，由协同设计原则驱动，即所解决的问题类型影响架构和设计的选择，计算机的局限性影响哪些计算问题适合量子计算。这项研究涉及一名本科生每年，五名研究生和一名博士后研究员在战略上重要的研究课题，支持积极破坏，即，迅速转变的计算机技术在许多科学、技术和其他领域产生了影响。这些学员将有机会与实验物理和计算机科学专家合作，为量子计算的学术和工业机会做好准备。 该项目旨在沿着以下途径推进量子计算的可扩展性，即设计高保真量子电路，为几十个量子比特的量子计算开发量子算法，为受BosonSampling问题启发的采样问题创建算法，阐明验证和验证以了解组件和设备符合标准并做它们应该做的事情，并建立保密性，互联量子事物的完整性和可用性。 在最后一种情况下，事物指的是组件、设备和系统，涉及量子网络、多方量子通信和云上的量子计算。 一名研究生将在这五个途径中的每一个上工作，一名博士后研究员将拥有宏伟的视野并将这些发现联系起来。 本科生将专注于解决技术问题。