1. A method performed by a quantum machine, comprising:receiving, as input at a quantum machine comprising an adiabatic quantum computing system having hardware connections comprising couplers that connect qubits included in the quantum machine, data for learning an inference in a model used in machine learning, wherein:
the model is a modified restricted Boltzmann machine that includes interactions among hidden units of the restricted Boltzmann machine, wherein the interactions are based on the hardware connections of the quantum machine;
the input maps at least some interactions of different interconnected units of the model to the hardware connections between qubits in the quantum machine; and
the input is derived using data for training the model and a state of the model, the data comprising observed data for training and validating the model; and
learning, by the quantum machine, the inference in the model; and
providing, as output from the quantum machine, data representing the learned inference.