Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial
  Pyramids
Cameron Braunstein Eddy Ilg Vladislav Golyanik
Quantum visual computing is advancing rapidly. This paper presents a new
formulation for stereo matching with nonlinear regularizers and spatial
pyramids on quantum annealers as a maximum a posteriori inference problem that
minimizes the energy of a Markov Random Field. Our approach is hybrid (i.e.,
quantum-classical) and is compatible with modern D-Wave quantum annealers,
i.e., it includes a quadratic unconstrained binary optimization (QUBO)
objective. Previous quantum annealing techniques for stereo matching are
limited to using linear regularizers, and thus, they do not exploit the
fundamental advantages of the quantum computing paradigm in solving
combinatorial optimization problems. In contrast, our method utilizes the full
potential of quantum annealing for stereo matching, as nonlinear regularizers
create optimization problems which are NP-hard. On the Middlebury benchmark, we
achieve an improved root mean squared accuracy over the previous state of the
art in quantum stereo matching of 2% and 22.5% when using different solvers.