Quantum mechanical hamiltonian learning and temporal property prediction

Abstract

A machine learning system for predicting a time dependent property of a material system includes a processor and a memory communicably coupled to the processor. Stored in the memory is an acquisition module and a machine learning module. The machine learning module includes instructions that, when executed by the processor, cause the processor during each of one or more iterations, to train a machine learning model to learn an initial state vector, Hermitian operators encoding observables, and a Hamiltonian of a material system from the Schrödinger equation of the material system propagated in a time series. The machine learning model also predicts, based at least in part on the learned initial state vector, Hermitian operators, and Hamiltonian, at least one time dependent property of the material system at time not equal to zero.