Z. Bian, et al. 

In this paper we investigate the use of hardware which physically realizes quantum annealing for machine learning applications. We show how to take advantage of the hardware in both zero- and finite-temperature modes of operation. At zero temperature the hardware is used as a heuristic minimizer of Ising energy functions, and at finite temperature the hardware allows for sampling from the corresponding Boltzmann distribution. We rely on quantum mechanical processes to perform both these tasks more efficiently than is possible through software simulation on classical computers. We show how Ising energy functions can be sculpted to solve a range of supervised learning problems. Finally, we validate the use of the hardware by constructing learning algorithms trained using quantum annealing on several synthetic and real data sets. We demonstrate that this novel approach to learning using quantum mechanical hardware can provide significant performance gains for a number of structured supervised learning problems.