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.

翻译：本文研究了基于含噪声中等规模量子（NISQ）器件的若干量子计算机视觉算法，并针对实际工业问题将其与经典对应算法进行了性能基准测试。具体而言，我们探讨了两种方法：基于通用门型量子计算机的量子支持向量机（QSVM）和基于量子退火器的QBoost算法。这些量子视觉系统在一个图像类别不平衡的数据集上进行了基准测试，其目标是检测汽车零部件制造中的缺陷。研究发现，量子算法在多个方面优于经典对应算法，其中QBoost使得当前量子退火器能够分析更大规模的问题。本文还讨论了数据预处理（包括降维和对比度增强）以及QBoost中的超参数调优。据我们所知，这是量子计算机视觉系统首次应用于制造生产线中的工业相关问题。