Towards Quantum Speedup for Solving High-Dimensional Partial Differential Equations

迈向求解高维偏微分方程的量子加速

• 批准号: 2208416
• 负责人: Di Fang
• 金额: $ 15万
• 依托单位: University of California-Berkeley
• 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: 2022-08-15 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208416&HistoricalAwards=false
• 关键词: Towards; Quantum; Speedup; Solving; Dimensional

Efficient simulation of high-dimensional partial differential equations has been one of the core tasks in many scientific areas. Recent advances of quantum technologies and algorithms revealed that quantum algorithms can be a new tool to overcome the curse of dimensionality, with the potential of achieving exponential speedups compared to classical implementations. The goal of this project is to investigate potential applications of quantum algorithms on efficiently solving high-dimensional differential equations. Taking advantage of the power of quantum mechanics, the newly proposed quantum algorithms and techniques are expected to significantly accelerate the simulation of such high-dimensional PDEs and give a cost depending poly-logarithmically on the total number of spatial grids and polynomially on the spatial dimension. The development of proposed projects will provide new prospects to overcome the curse of dimensionality in PDE simulations, to advance the state-of-the-art quantum algorithm designed for differential equations, and to help pave the path towards post-quantum scientific computing. On the educational side, the students involved will get good interdisciplinary training in both mathematics and quantum information science.This project aims to develop efficient quantum algorithms for high-dimensional differential equations for both quantum and classical problems, and to establish rigorous error bounds and complexity estimates, and identify the problems that can and can not be efficiently handled quantumly. Such high-dimensional differential equations include the Schrodinger equation with applications to molecular dynamics, and other classical differential equations, such as reaction-diffusion equations emerging from biological applications. The following specific aspects will be addressed. For quantum dynamics simulation, the goal is to deal with dynamics simulation with unbounded operators, we explore techniques such as the vector norm scaling analysis, quantum highly oscillatory protocol in the interaction picture, and semiclassical/microlocal analysis addressing the multiscale aspects of the problem. For classical dynamics that can be non-unitary, we propose and explore time-marching strategies using block encoding oracles, and aim to provide a pedagogical description on quantum algorithms for stiff differential equations, pinpointing the differences between quantum algorithm design and classical numerical analysis.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.

高维偏微分方程的高效模拟已成为许多科学领域的核心任务之一。量子技术和算法的最新进展表明，量子算法可以成为克服维度诅咒的新工具，与经典实现相比，具有实现指数级速度的潜力。该项目的目标是研究量子算法在高效求解高维微分方程方面的潜在应用。利用量子力学的力量，新提出的量子算法和技术有望显著加速此类高维偏微分方程的模拟，并给出基于空间网格总数的多对数和基于空间维度的多项式的成本。拟议项目的发展将为克服PDE模拟中的维度诅咒提供新的前景，推进为微分方程设计的最先进的量子算法，并帮助为后量子科学计算铺平道路。在教育方面，参与的学生将在数学和量子信息科学方面得到良好的跨学科培训。本项目旨在为量子和经典问题的高维微分方程开发有效的量子算法，建立严格的误差界限和复杂性估计，并确定可以和不能有效地处理量子问题。这种高维微分方程包括应用于分子动力学的薛定谔方程，以及其他经典微分方程，如生物应用中的反应扩散方程。将讨论以下几个具体方面。对于量子动力学模拟，目标是处理具有无界算子的动力学模拟，我们探索了诸如矢量范数标度分析，相互作用图像中的量子高振荡协议以及解决问题的多尺度方面的半经典/微局部分析等技术。对于非酉的经典动力学，我们提出并探索了使用块编码预言器的时间推进策略，并旨在为刚性微分方程的量子算法提供教学描述，指出量子算法设计与经典数值分析之间的差异。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Observable Error Bounds of the Time-Splitting Scheme for Quantum-Classical Molecular Dynamics

量子经典分子动力学时间分割方案的可观测误差界

• DOI: 10.1137/21m1462349
• 发表时间: 2023
• 期刊: SIAM Journal on Numerical Analysis
• 影响因子: 2.9
• 作者: Fang, Di;Vilanova, Albert Tres
• 通讯作者: Vilanova, Albert Tres

Time-marching based quantum solvers for time-dependent linear differential equations

基于时间推进的时间相关线性微分方程的量子求解器

• DOI: 10.22331/q-2023-03-20-955
• 发表时间: 2023
• 期刊: Quantum
• 影响因子: 6.4
• 作者: Fang, Di;Lin, Lin;Tong, Yu
• 通讯作者: Tong, Yu