Software and algorithms for enabling noise-aware quantum computation on near-term devices

用于在近期设备上实现噪声感知量子计算的软件和算法

• 批准号: RGPIN-2022-04609
• 负责人: DiMatteo, Olivia
• 金额: $ 1.82万
• 依托单位: University of British Columbia
• 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=752078
• 关键词: Software; algorithms; enabling; noise; aware

Quantum computing is an interdisciplinary field that is developing a new type of computing device based on a quantum system called a qubit. Quantum computers (QCs) will soon solve problems that are intractable for regular computers at a large scale, such as simulating chemical reactions, searching through large spaces, and optimizing industrial processes. Numerous QCs with 50-60 usable qubits are now available, which is a significant development, but far from the thousands to millions of qubits needed to solve "life-sized" problems. Building QCs is a major engineering challenge; building high-quality ones in a scalable manner even more so. Qubits are extremely noisy due to their high sensitivity to external effects. Any interaction with the environment, such as thermal energy, material defects, or simply the presence of a nearby qubit being manipulated, can lead to decoherence of a qubit's state, rendering it unusable for computation until it is reset. Until more precise control becomes possible and measures such as error-correction can be implemented, it is critical that our algorithms are designed to make the best use of today's devices despite the presence of noise. The goal of my research program is to develop and apply new software and characterization techniques to mitigate and adapt to noise in QCs. Specifically, my team will: i) design and implement noise-aware techniques for compiling quantum algorithms to run on hardware, ii) improve and extend quantum tomography methods to characterize noise, and leverage them to support the development of novel quantum hardware at UBC, and iii) use these results to design robust quantum algorithms for such state-of-the-art devices. We will accomplish this by implementing and benchmarking our methods with open-source quantum programming frameworks, running on both regular computers and on quantum hardware. Furthermore, we will contribute our methods back to the community through open-source software and open-access research results. The innovative techniques developed in my program will lead to improved software and algorithms for general-purpose QCs, as well as algorithms tailored to UBC's in-house hardware. This work is critical to demonstrate the utility of QCs built over the upcoming decade, and paves the way for future generations of quantum hardware. This work will be carried out by a newly-assembled group of diverse and interdisciplinary student researchers, in collaboration with myself and other UBC faculty members working on quantum software and hardware. Over the next five years, I intend to train 3 PhD students, 2 MSc students, and 6 undergraduates. Their academic work will be augmented by industry internships and participation in educational opportunities and public outreach, in order to prepare them for careers in the growing quantum computing ecosystem in Canada and beyond.

量子计算是一个跨学科领域，正在开发一种基于称为量子比特的量子系统的新型计算设备。量子计算机（QCs）将很快解决常规计算机难以解决的大规模问题，例如模拟化学反应，搜索大空间和优化工业流程。现在有许多具有50-60个可用量子位的QC可用，这是一个重大的发展，但远远没有解决“生命大小”问题所需的数千到数百万量子位。构建QC是一项重大的工程挑战;以可扩展的方式构建高质量的QC更是如此。量子比特由于其对外部效应的高度敏感性而具有极强的噪声。与环境的任何相互作用，例如热能，材料缺陷，或者仅仅是附近被操纵的量子位的存在，都可能导致量子位状态的退相干，使其在重置之前无法用于计算。在更精确的控制成为可能并且可以实施纠错等措施之前，至关重要的是，我们的算法旨在充分利用当今的设备，尽管存在噪声。我的研究计划的目标是开发和应用新的软件和表征技术，以减轻和适应QC中的噪声。具体而言，我的团队将：i）设计和实现噪声感知技术，用于编译量子算法在硬件上运行，ii）改进和扩展量子层析成像方法以表征噪声，并利用它们来支持UBC新型量子硬件的开发，以及iii）使用这些结果为这种最先进的设备设计鲁棒的量子算法。我们将通过使用开源量子编程框架实现和基准测试我们的方法来实现这一目标，这些框架在常规计算机和量子硬件上运行。此外，我们将通过开源软件和开放获取的研究成果将我们的方法回馈给社区。在我的程序中开发的创新技术将导致改进的软件和算法的通用QC，以及算法量身定制UBC的内部硬件。这项工作对于展示未来十年建造的QC的实用性至关重要，并为未来几代量子硬件铺平了道路。这项工作将由一个新组建的多元化和跨学科的学生研究小组进行，与我和其他UBC教职员工合作，研究量子软件和硬件。在接下来的五年里，我打算培养3名博士生，2名硕士生和6名本科生。他们的学术工作将通过行业实习和参与教育机会和公共宣传来增强，以便为他们在加拿大及其他地区不断增长的量子计算生态系统中的职业生涯做好准备。