  In this paper we consider several algorithms for quantum computer vision
using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a
real problem against their classical counterparts. Specifically, we consider
two approaches: a quantum Support Vector Machine (QSVM) on a universal
gate-based quantum computer, and QBoost on a quantum annealer. The quantum
vision systems are benchmarked for an unbalanced dataset of images where the
aim is to detect defects in manufactured car pieces. We see that the quantum
algorithms outperform their classical counterparts in several ways, with QBoost
allowing for larger problems to be analyzed with present-day quantum annealers.
Data preprocessing, including dimensionality reduction and contrast
enhancement, is also discussed, as well as hyperparameter tuning in QBoost. To
the best of our knowledge, this is the first implementation of quantum computer
vision systems for a problem of industrial relevance in a manufacturing
production line.
