  The design and performance of computer vision algorithms are greatly
influenced by the hardware on which they are implemented. CPUs, multi-core
CPUs, FPGAs and GPUs have inspired new algorithms and enabled existing ideas to
be realized. This is notably the case with GPUs, which has significantly
changed the landscape of computer vision research through deep learning. As the
end of Moores law approaches, researchers and hardware manufacturers are
exploring alternative hardware computing paradigms. Quantum computers are a
very promising alternative and offer polynomial or even exponential speed-ups
over conventional computing for some problems. This paper presents a novel
approach to image segmentation that uses new quantum computing hardware.
Segmentation is formulated as a graph cut problem that can be mapped to the
quantum approximate optimization algorithm (QAOA). This algorithm can be
implemented on current and near-term quantum computers. Encouraging results are
presented on artificial and medical imaging data. This represents an important,
practical step towards leveraging quantum computers for computer vision.
