Quantum Computing for Computational Fluid Dynamics

计算流体动力学的量子计算

• 批准号: 2752282
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2752282
• 关键词: Quantum; Computing; Computational; Fluid; Dynamics

Many of the environmental and energy-related issues we face today cannot possibly be tackled without a better understanding of the dynamics of fluids. Understanding, predicting and controlling fluid flows is of central importance and a limiting factor to a vast range of industries:naval, aeronautical, automotive, power generation, process, pharmaceutical, meteorological, environmental, etc. Simulating and understanding fluid flows is one of the most challenging problems in science. Significant progress has been made recently using High Performance Computing, and Computational Fluid Dynamics is now a critical complement to experiments and theories.While the Navier-Stokes equations constitute a broadly accepted mathematical model to describe the motions of a turbulent flow, their solutions can be extremely challenging to obtain due to the chaotic and inherently multi-scale nature of turbulence. However, even with today's state-of-the-art algorithms and modern supercomputers, accurate simulations of turbulent flows are only feasible for a small class of problems, at low speeds in simple geometries.As such simulations are computationally expensive, the quest of highly efficient Computational Fluid Dynamics algorithms remains an open question for the community. It is therefore natural to explore and provide ideas about how quantum computers algorithms should be applied to solving Computational Fluid Dynamics problems.Quantum computers are based on quantum mechanical phenomena such as superposition and entanglement to perform. Because they compute in ways that classical computers cannot, quantum algorithms can provide exponential speedups over their classical counterparts.This project will investigate the potential of quantum computers algorithms applied to Computational Fluid Dynamics problems. The two main tasks to be carried out are: (1) Identify which parts of a classical flow solver can be replaced by quantum computers algorithms, taking into account the potential computational gains that the algorithm would bring with respect to the standard algorithm (quantum advantage), (2) develop, test and validate such algorithms, via a lightweight expression template library (see the LibKet library as a primitive example). The idea is to propose quantum algorithms as backend-agnostic generic expressions and execute them on different distributed hardware backends without changing the code. Such library will make it possible to formulate quantum algorithms in an abstract way, as opposed to express quantum algorithms using low-level quantum gates for a particular language and quantum hardware.

如果不更好地了解流体动力学，我们今天面临的许多环境和能源相关问题就不可能得到解决。理解、预测和控制流体流动是至关重要的，也是许多行业的限制因素：海军、航空、汽车、发电、工艺、制药、气象、环境等。模拟和理解流体流动是科学中最具挑战性的问题之一。最近使用高性能计算取得了重大进展，计算流体动力学现在是实验和理论的重要补充。虽然Navier-Stokes方程构成了一个被广泛接受的描述湍流运动的数学模型，但由于湍流的混沌和固有的多尺度性质，其解可能极具挑战性。然而，即使使用当今最先进的算法和现代超级计算机，湍流的精确模拟也只适用于一小部分问题，在简单几何形状的低速下。由于这样的模拟在计算上是昂贵的，对高效计算流体动力学算法的追求仍然是社区的一个开放问题。因此，探索和提供关于如何将量子计算机算法应用于解决计算流体动力学问题的想法是很自然的。量子计算机是基于量子力学的叠加和纠缠等现象来执行的。因为量子算法的计算方式是经典计算机无法做到的，所以它可以提供比经典计算机指数级的速度。这个项目将研究量子计算机算法应用于计算流体动力学问题的潜力。要执行的两个主要任务是：(1)确定经典流求解器的哪些部分可以被量子计算机算法取代，考虑到该算法相对于标准算法（量子优势）可能带来的计算收益；(2)通过轻量级表达式模板库（参见LibKet库作为原始示例）开发、测试和验证此类算法。其想法是将量子算法作为与后端无关的泛型表达式，并在不更改代码的情况下在不同的分布式硬件后端上执行它们。这样的库将使以抽象的方式表述量子算法成为可能，而不是使用特定语言和量子硬件的低级量子门来表达量子算法。