Research on Quantum Circuit Learning Models for Molecular Dynamics Simulation

In the past decade, many hybrid quantum-classical algorithms have been applied to various industrial fields, reflecting the growing interest in NISQ devices. For example, in the field of chemistry, many studies were conducted to calculate the energies of eigenstates with high precision. However, there is still little research on molecular dynamics using quantum computers, because traditional approaches based on variational quantum algorithms are not suitable for the long-time steps of molecular dynamics. In this study, we propose a quantum circuit learning model that estimates the eigenvalues of a given molecular Hamiltonian for an arbitrary molecular configuration. Our model must first be costly to train; however, once trained, the resulting trained model can eliminate procedures that required variational optimization each time the molecular coordinates are updated in the previous variational approach. By using the proposed model, we demonstrate that the proposed model can be used to perform simple molecular dynamics simulations, such as Langevin dynamics and NVE simulation. We also show several applications of our model inspired by classical machine learning.