Quantum Computing based density functionals for fast and accurate materials and chemistry simulations

基于量子计算的密度泛函，用于快速、准确的材料和化学模拟

• 批准号: 10074167
• 金额: $ 39.64万
• 依托单位: INSTADEEP LTD
• 依托单位国家: 英国
• 项目类别: Feasibility Studies
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10074167
• 关键词: Quantum; Computing; based; density; functionals

Quantum computers have the potential to lead to a transformative increase in the available computational power. The size and quality of the quantum computers available today are increasing rapidly, and advancements in ability to implement quantum error correction are getting us closer to have quantum computers which will be able to outperform even the best existing classical computers. One of the most promising improvements offered by quantum computers is the ability to accurately simulate atomic scale physical systems, which is a challenging task for classical computers. Various algorithms are being developed today that aim to make use of this advantage that is offered by quantum computers.Density functional theory (DFT) is a core classical computing method for product development across industries such as pharmaceuticals, chemicals, and materials. For instance, DFT can be used to model and evaluate the properties of new materials, drugs, catalysts, etc. to complement expensive developments in the lab. Compared to alternative methods, DFT is the only approach fast enough for large systems comprising metals and molecules as required in many applications. However, approximations made in the DFT algorithms used today make them unsuitable for a lot of important problems. Improvements in DFT can lead to the development of better batteries, more efficient catalysts, faster and more reliable drug and vaccine candidate assessments, and greener chemical production.Recently, machine learning (ML) methods have been demonstrated to lead to improvements in DFT. However, even the ML + DFT methods rely on a large number of accurate simulations of the physical systems, which are challenging to do with classical computers. In this project, we aim to harness the superior ability of quantum computers to perform accurate simulations in order to improve DFT. Our approach will combine the advantages provided by quantum computers and ML in order to significantly improve the DFT method that is widely used in various industries.This project combines the expertise in machine learning, quantum computing, DFT, and modelling in industrial settings of InstaDeep, NPL, Atos UK, and Johnson Matthey.

量子计算机有可能导致可用计算能力的变革性增长。今天可用的量子计算机的大小和质量正在迅速增长，实现量子纠错能力的进步使我们离拥有能够超越甚至最好的现有经典计算机的量子计算机更近了一步。量子计算机提供的最有希望的改进之一是能够准确地模拟原子尺度的物理系统，这对经典计算机来说是一项具有挑战性的任务。为了利用量子计算机提供的这一优势，目前正在开发各种算法。密度泛函理论(DFT)是跨制药、化工和材料等行业产品开发的核心经典计算方法。例如，DFT可以用于对新材料、药物、催化剂等的性能进行建模和评估，以补充实验室中昂贵的开发。与其他方法相比，对于许多应用中需要的由金属和分子组成的大系统，DFT是唯一足够快的方法。然而，目前使用的DFT算法中的近似使得它们不适合于许多重要的问题。DFT的改进可以导致更好的电池、更高效的催化剂、更快和更可靠的候选药物和疫苗评估以及更绿色的化学生产。最近，机器学习(ML)方法已被证明导致DFT的改进。然而，即使是ML+DFT方法也依赖于对物理系统的大量精确模拟，这对于经典计算机来说是具有挑战性的。在这个项目中，我们的目标是利用量子计算机的优越能力来执行精确的模拟，以改进DFT。我们的方法将结合量子计算机和ML的优势，以显著改进广泛应用于各个行业的DFT方法。该项目结合了InstaDeep、NPL、Atos UK和Johnson Matthee在工业环境中的机器学习、量子计算、DFT和建模方面的专业知识。