Quantum Machine-Learning algorithm for Complex Chemical Systems

In this talk, I will give a brief  overview of developing quantum algorithms for electronic structure calculations and open quantum dynamics of chemical systems on quantum devices. Then focus on quantum machine learning, particularly the Restricted Boltzmann Machine (RBM), as it  emerged to be a promising alternative approach  leveraging  the power of quantum computers. To demonstrate its efficacy, we show its performance on calculating electronic structure of small molecular systems like 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 also discuss extending the approach to treat complex open quantum dynamical processes. We thus expect our protocol to provide a new alternative in exploring electronic structure and dynamics of complex chemical systems.