Training and classification using Restricted Boltzmann Machine (RBM) on the D-Wave 2000Q

Training and classification of a restricted Boltzmann machine (RBM) has been performed using D-Wave system. RBM is an energy-based model, which assigns low energy values to the configurations of interest. The D-Wave 2000Q is an adiabatic quantum computer which has been used to obtain samples for the gradient learning. Two datasets namely ‘bars and stripes (BAS)’ and ‘solar farm (PV)’ have been used. For BAS dataset, objective is to classify a given pattern as bars or stripes, while for PV dataset the goal is to predict “efficiency degradation” based on some model parameters. Results are compared with RBM trained using standard contrastive divergence. Classification and data reconstruction are also presented. Estimated classification accuracies indicate comparable performance of the both methods. D-Wave training seems to result in smaller weights, thus reduces overfitting problems.