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Many artificial intelligence (AI) problems naturally map to NP-hardoptimization problems. This has the interesting consequence that enablinghuman-level capability in machines often requires systems that can handleformally intractable problems. This issue can sometimes (but possibly notalways) be resolved by building special-purpose heuristic algorithms, tailoredto the problem in question. Because of the continued difficulties in automatingcertain tasks that are natural for humans, there remains a strong motivationfor AI researchers to investigate and apply new algorithms and techniques tohard AI problems. Recently a novel class of relevant algorithms that requirequantum mechanical hardware have been proposed. These algorithms, referred toas quantum adiabatic algorithms, represent a new approach to designing bothcomplete and heuristic solvers for NP-hard optimization problems. In this workwe describe how to formulate image recognition, which is a canonical NP-hard AIproblem, as a Quadratic Unconstrained Binary Optimization (QUBO) problem. TheQUBO format corresponds to the input format required for D-Wave superconductingadiabatic quantum computing (AQC) processors.

Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization