A central goal in quantum computing is the development of quantum hardware and quantum algorithms in order to analyse challenging scientific and engineering problems. Research in quantum computation involves contributions from both physics and computer science, hence this article presents a concise introduction to basic concepts from both fields that are used in annealing-based quantum computation, an alternative to the more familiar quantum gate model.We introduce some concepts from computer science required to define difficult computational problems and to realise the potential relevance of quantum algorithms to find novel solutions to those problems. We introduce the structure of quantum annealing-based algorithms as well as two examples of this kind of algorithms for solving instances of the max-SAT and Minimum Multicut problems. An overview of the quantum annealing systems manufactured by D-Wave Systems is also presented.Quantum annealing is a physical platform of quantum computation focused on solving combinatorial optimisation problems. Quantum annealing is a restricted form of adiabatic quantum computation but the problems that can be explored using this paradigm are vast and relevant to many fields of science and technology. D-Wave systems manufactures commercially available quantum annealingbased hardware on which it is possible to run algorithms. Thanks to the access granted by USRA-NASA to the D-Wave's quantum annealer installed at NASA Ames Research Centre, our work on quantum annealing-based algorithms and the examples presented in this paper have been designed, tested and run using both D-Wave's quantum annealer and D-Wave's advanced simulation software. Future quantum annealers probably will use similar hardware and programming principles as D-Wave's, so we expect that our discussions and results will have a broad impact in the field of annealingbased quantum computation.

In this paper, we propose a methodology to solve the stereo matching problem through quantum annealing optimization. Our proposal takes advantage of the existing Min-Cut/Max-Flow network formulation of computer vision problems. Based on this network formulation, we construct a quadratic pseudo-Boolean function and then optimize it through the use of the D-Wave quantum annealing technology. Experimental validation using two kinds of stereo pair of images, random dot stereograms and gray-scale, shows that our methodology is effective.

Human posture classification is an important tasks in medical applications, i.e., patient monitoring, ulcer prevention, and conduct diagnostic. We propose a system for posture recognition of lying-down human bodies using a low-resolution pressure sensor array. A support vector-machine was used to perform the classification of pressure maps. Three databases were constructed in order to represent the pressure maps: pressure raw-data, HOG and SIFT image descriptor vectors. It was found that the image descriptors have improved complexity time to build the classification models rather than using raw pressure maps. Experimental results was performed in order to control a robotic hospital bed.

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