Quantum machine learning have emerged to be a promising alternative to their classical counterparts as they leverage the power of quantum computers. In recent times, a plethora of classical machine learning methods have been developed to solve problems like accurate calculations of ground state of molecular systems and spin models in magnetic systems. Herein we demonstrate a quantum algorithm that can filter not only the ground state of the target system, but any energy eigenstates of the system based on either symmetry properties or on a predefined choice of the user. The work horse of our technique is a shallow neural network encoding the desired state of the system with the amplitude for the state computed by sampling the Gibbs-Boltzmann distribution using a quantum circuit and the phase information is obtained classically from the non-linear activation of a separate set of neurons. We show that the resource requirements of our algorithm are strictly quadratic. To demonstrate its efficacy, we show its performance on small molecular systems like H2, LiH, H2O and also computation of band structures in 2D materials like graphene, h-BN and monolayer transition-metal di-chalcogenides which are hitherto unexplored in quantum simulations. We implement our algorithm not only on quantum simulators but also on actual IBM-Q quantum devices and show good agreement with the results procured from conventional electronic structure calculations. We thus expect our protocol to provide a new alternative in exploring the chemistry of molecules as well as exquisite materials to usual electronic structure methods or machine learning techniques which are implementable solely on a classical computer.

Quantum machine-learning algorithm for evaluating electronic structure of molecules and materials

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.