BI-DIRECTIONAL QUANTUM ANNEALING IN MARKOV RANDOM FIELDS FOR MACHINE LEARNING IN IMAGE ANALYSIS

Methods, systems, and apparatus for a bi-directional quantum annealing approach to Markov random field networks for machine learning in image analysis. In one aspect, a method includes obtaining training data comprising features extracted from a first set of images; training a deep quantum restricted Boltzmann machine (QRBM) comprising multiple layers using the training data, the training comprising layer-wise training of the multiple layers, wherein training each layer of the multiple layers comprises evaluating a restricted Boltzmann machine (RBM) probability distribution using bi-directional quantum annealing; and validating the trained deep QRBM using test data comprising features extracted from a second set of images.