Developing Quantum Optimisation Algorithms

开发量子优化算法

• 批准号: 2284239
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2284239
• 关键词: Developing; Quantum; Optimisation; Algorithms

This project is concerned with solving non-linear partial differential equations (PDEs) on a quantum computer. Recently, it has been shown that an exponential speedup in conventional multigrid (MG) methods for solving non-linear PDEs on a classical computer can be obtained via MG renormalization (MGR) methods [Journal of Computational Physics 372, 587 (2018)]. In this project, the DPhil student will implement MGR methods on a quantum computer focussing on industrially relevant problems in fluid dynamics. Based on current timetables for the future availability of quantum hardware (e.g. through our partnership with IBM), we expect our quantum algorithms to outperform classical computers within the next 3 - 5 years. Since non-linear PDEs are ubiquitous in all areas of science and technology, our method has the potential to leverage the use of quantum-enhanced algorithms in a broad range of real-world applications. The main tasks are:(i) developing optimized implementations of algorithms for solving non-linear PDEs on existing quantum devices aiming to achieve an exponential speedup. This entails developing platform-specific algorithms mitigating their individual weaknesses such that a quantum advantage can be obtained. The student will consider quantum devices based on trapped ions and in particular the Q20:20 machine, as well as devices on IBM's quantum computing network.(ii) identifying industrially relevant applications where MGR methods on a quantum computer could help solving outstanding problems or improve the quality of existing solutions. This part of the project will be carried out in close collaboration with BAE Systems, who are interested in solving complex fluid dynamics problems that are modelled by non-linear PDEs.This project falls within the EPSRC Quantum Technologies research area.

该项目涉及在量子计算机上求解非线性偏微分方程（PDE）。最近，研究表明，在经典计算机上求解非线性偏微分方程的传统多重网格 (MG) 方法可以通过 MG 重整化 (MGR) 方法获得指数加速 [Journal of Computational Chemistry 372, 587 (2018)]。在这个项目中，博士生将在量子计算机上实施 MGR 方法，重点关注流体动力学中的工业相关问题。根据当前量子硬件未来可用性的时间表（例如通过我们与 IBM 的合作），我们预计我们的量子算法将在未来 3 - 5 年内超越经典计算机。由于非线性偏微分方程在科学技术的所有领域中普遍存在，因此我们的方法有可能在广泛的现实应用中利用量子增强算法。主要任务是：（i）开发算法的优化实现，用于在现有量子设备上求解非线性偏微分方程，旨在实现指数加速。这需要开发特定于平台的算法，以减轻其各自的弱点，从而获得量子优势。学生将考虑基于捕获离子的量子设备，特别是 Q20:20 机器，以及 IBM 量子计算网络上的设备。(ii) 确定工业相关应用，其中量子计算机上的 MGR 方法可以帮助解决突出问题或提高现有解决方案的质量。该项目的这一部分将与 BAE Systems 密切合作，该公司对解决由非线性偏微分方程建模的复杂流体动力学问题感兴趣。该项目属于 EPSRC Quantum Technologies 研究领域。