EAGER: QSA: Accelerating Lattice Quantum Field Theory Calculations Via Interpolator Optimization Using NISQ-Era Quantum Computing

EAGER：QSA：使用 NISQ-Era 量子计算通过插值器优化加速晶格量子场论计算

• 批准号: 2035015
• 负责人: Phiala Shanahan
• 金额: $ 20万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035015&HistoricalAwards=false
• 关键词: EAGER; QSA; Accelerating; Lattice; Quantum

Simulations of physical quantum systems are one of the promising and natural potential applications of quantum computing. At the present time, significant effort is being devoted to developing quantum algorithms for quantum physics. Complementing exclusively quantum calculations, there is great potential for quantum advantages to be obtained by integrating near-term quantum devices into existing classical workflows, much as has been achieved by the integration of graphical processing units as accelerators into high-performance computing systems in the last decades. This project investigates one such pathway via the acceleration of numerical quantum field theory calculations on classical computers using quantum computing. The developed algorithms have the potential to enable first-principles studies of the structure of matter that are computationally intractable via classical means.The primary goal of this project is to implement a quantum algorithm to accelerate lattice field theory calculations via the efficient identification of representations of quantum states. The approach taken is demonstrably robust to errors in the calculations performed on quantum devices, and enables scaling to larger Hilbert spaces to be undertaken on classical rather than quantum devices. Strategies to realize the algorithm are pursued and implemented on superconducting qubit devices, as well as on realistic simulators. A key task is to quantify the scaling and implementation costs of the approach. Success in this development effort has the potential to address the exponential signal-to-noise challenge in numerical studies of the quantum field theories which describe nature. The broader impacts of this project are focused on training the next generation of scientists whose expertise will bridge the intersection of quantum computing and algorithms and physics.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.

物理量子系统的模拟是量子计算最有前途和自然的潜在应用之一。目前，人们正在致力于开发量子物理学的量子算法。作为对量子计算的补充，通过将近期量子设备集成到现有的经典工作流程中，可以获得量子优势的巨大潜力，就像过去通过将图形处理单元作为加速器集成到高性能计算系统中所实现的那样。几十年。该项目通过使用量子计算在经典计算机上加速数值量子场论计算来研究这样一种途径。所开发的算法有潜力能够对通过经典方法难以计算的物质结构进行第一性原理研究。该项目的主要目标是实现一种量子算法，通过有效识别量子态。所采用的方法对于在量子设备上执行的计算中的错误具有明显的鲁棒性，并且能够在经典设备而不是量子设备上扩展到更大的希尔伯特空间。在超导量子位设备以及现实模拟器上寻求和实现实现该算法的策略。一项关键任务是量化该方法的扩展和实施成本。这项开发工作的成功有可能解决描述自然的量子场论数值研究中指数级信噪比的挑战。该项目的更广泛影响集中在培训下一代科学家，他们的专业知识将在量子计算、算法和物理学之间架起桥梁。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。