Classical computing has borne witness to the development of machine learning. The integration of quantum technology into this mix will lead to unimaginable benefits and be regarded as a giant leap forward in mankind's ability to compute. Demonstrating the benefits of this integration now becomes essential. With the advance of quantum computing, several machine-learning techniques have been proposed that use quantum annealing. In this study, we implement a matrix factorization method using quantum annealing for image classification and compare the performance with traditional machine-learning methods. Nonnegative/binary matrix factorization (NBMF) was originally introduced as a generative model, and we propose a multiclass classification model as an application. We extract the features of handwritten digit images using NBMF and apply them to solve the classification problem. Our findings show that when the amount of data, features, and epochs is small, the accuracy of models trained by NBMF is superior to classical machine-learning methods, such as neural networks. Moreover, we found that training models using a quantum annealing solver significantly reduces computation time. Under certain conditions, there is a benefit to using quantum annealing technology with machine learning.

翻译：经典计算见证了机器学习的发展。将量子技术融入其中将带来难以想象的益处，并被视为人类计算能力的巨大飞跃。如今，证明这种融合的益处变得至关重要。随着量子计算的进步，已有多项利用量子退火的机器学习技术被提出。在本研究中，我们采用量子退火实现了一种用于图像分类的矩阵分解方法，并与传统机器学习方法的性能进行了比较。非负/二元矩阵分解（NBMF）最初是作为一种生成模型提出的，我们将其扩展为多类分类模型作为应用。我们利用NBMF提取手写数字图像的特征，并将其应用于分类问题的求解。研究结果表明：当数据量、特征数量和训练轮次较少时，经NBMF训练的模型在准确率上优于神经网络等经典机器学习方法。此外，我们发现使用量子退火求解器训练模型能显著缩短计算时间。在特定条件下，将量子退火技术与机器学习相结合具有显著优势。