Quantum Annealing for Computer Vision Minimization Problems
Shahrokh Heidari Michael J. Dinneen Patrice Delmas
Computer Vision (CV) labelling algorithms play a pivotal role in the domain
of low-level vision. For decades, it has been known that these problems can be
elegantly formulated as discrete energy minimization problems derived from
probabilistic graphical models (such as Markov Random Fields). Despite recent
advances in inference algorithms (such as graph-cut and message-passing
algorithms), the resulting energy minimization problems are generally viewed as
intractable. The emergence of quantum computations, which offer the potential
for faster solutions to certain problems than classical methods, has led to an
increased interest in utilizing quantum properties to overcome intractable
problems. Recently, there has also been a growing interest in Quantum Computer
Vision (QCV), with the hope of providing a credible alternative or assistant to
deep learning solutions in the field. This study investigates a new Quantum
Annealing based inference algorithm for CV discrete energy minimization
problems. Our contribution is focused on Stereo Matching as a significant CV
labeling problem. As a proof of concept, we also use a hybrid quantum-classical
solver provided by D-Wave System to compare our results with the best classical
inference algorithms in the literature.