Implementation of quantum machine learning for electronic structure calculations of periodic systems on NISQ devices

Recent progress in the development of quantum machine learning algorithms has attracted a lot of attention especially for the purpose of electronic structure calculations. These algorithms demonstrate accurate electronic structure calculations of lattice models, molecular systems, and has also been extended to periodic systems. Among these, a hybrid approach using Restricted Boltzmann Machine (RBM) and a quantum algorithm to optimize the objective function is a promising method due to its efficiency and ease of implementation. However, implementing these algorithms based on the RBM approach on an actual quantum computer requires a modification since only one ancilla qubit is not sufficient. We present the modified approach that can be implemented on Noisy Intermediate-Scale Quantum (NISQ) devices along with the results of implementing this method on IBM-Q for the computation of the electronic structure of graphene.