A hybrid approach to quantum dynamics based on the integration of quantum calculations and machine learning

基于量子计算和机器学习集成的量子动力学混合方法

• 批准号: RGPIN-2020-04969
• 负责人: Krems, Roman
• 金额: $ 4.66万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717384
• 关键词: hybrid; approach; quantum; dynamics; based

Quantum mechanics provides the most accurate description of molecular dynamics that determine microscopic chemical reactions and the structure and functionality of quantum materials. However, the fully quantum description of complex molecular systems is difficult. The present work aims to capitalize on recent developments in quantum dynamics theory and in machine learning in order to develop new approaches to quantum dynamics. The goal is to develop quantum approaches that will (1) require less computational resources than currently established approaches; (2) produce not only quantum observables but also the uncertainties of these observables stemming from limitations imposed by the difficulty of solving the Schrodinger equation; (3) combine quantum theory of molecular dynamics with experimental observations in order to produce improved descriptions of microscopic molecular interactions; (4) make accurate predictions of quantum observables for systems and experimental conditions that are currently out of reach of rigorous quantum theory. These goals will be achieved by integrating Bayesian machine learning (ML) into the methodology of quantum calculations. This will produce a flexible framework for quantum dynamics calculations. The core of this framework will be the nuclear Schrodinger equation. However, the inputs into the Schrodinger equation will be designed by ML models and the results of the quantum calculations will be processed by another ML model. This will dramatically reduce the number of quantum calculations required for accurate predictions of dynamical properties. Moreover, this will allow for new, currently unfeasible, problems to be solved. Specifically, we will aim to address the following major challenges in quantum molecular dynamics: (i) The inverse scattering problem aiming to obtain accurate potentials for microscopic molecular interactions from experimental observables. (ii) System-agnostic construction of global potential energy surfaces for very high-dimensional systems (up to 100 dimensions). (iii) Improving the accuracy of quantum predictions based on approximate dynamical approaches by combining the machine learning models that interpolate and generalize approximate results with machine learning models that infer the difference between the approximate results and rigorous or experimental results. (iv) Understand how to use emerging quantum computing devices for applications in molecular dynamics. Our work will provide general tools to make quantum predictions for bigger systems and with better accuracy than currently feasible. This could be a key advance for numerous research fields, ranging from drug design, to catalysis, to chemical kinetics. Our work will link emerging quantum computing technologies and molecular dynamics, paving a way for a new application for quantum technologies. This will contribute to maintaining Canada's leadership position in practical quantum computing industry.

量子力学提供了对分子动力学最准确的描述，分子动力学决定了微观化学反应以及量子材料的结构和功能。然而，复杂分子系统的完全量子描述很困难。 目前的工作旨在利用量子动力学理论和机器学习的最新发展，以开发量子动力学的新方法。我们的目标是开发量子方法，这些方法（1）比当前建立的方法需要更少的计算资源； (2) 不仅产生量子可观测量，而且还产生这些可观测量的不确定性，这些不确定性源于求解薛定谔方程的困难所带来的限制； （3）将分子动力学的量子理论与实验观察相结合，以改进对微观分子相互作用的描述； （4）对目前严格量子理论无法达到的系统和实验条件进行量子可观测量的准确预测。 这些目标将通过将贝叶斯机器学习 (ML) 集成到量子计算方法中来实现。这将为量子动力学计算产生一个灵活的框架。该框架的核心将是核薛定谔方程。然而，薛定谔方程的输入将由机器学习模型设计，量子计算的结果将由另一个机器学习模型处理。这将大大减少准确预测动力学特性所需的量子计算数量。此外，这将有助于解决目前不可行的新问题。 具体来说，我们将致力于解决量子分子动力学中的以下主要挑战： (i) 逆散射问题，旨在从实验可观测值中获得微观分子相互作用的准确势。 (ii) 极高维系统（高达 100 维）的全局势能面的系统不可知构造。 (iii) 通过将插值和概括近似结果的机器学习模型与推断近似结果与严格或实验结果之间差异的机器学习模型相结合，提高基于近似动力学方法的量子预测的准确性。 (iv) 了解如何将新兴的量子计算设备用于分子动力学应用。 我们的工作将提供通用工具来对更大的系统进行量子预测，并且比目前可行的精度更高。这可能是从药物设计到催化再到化学动力学等众多研究领域的关键进步。我们的工作将把新兴的量子计算技术和分子动力学联系起来，为量子技术的新应用铺平道路。这将有助于保持加拿大在实用量子计算行业的领导地位。