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

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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.