Over the past few years, there has been significant interest in Quantum Machine Learning (QML) among researchers, as it has the potential to transform the field of machine learning. Several models that exploit the properties of quantum mechanics have been developed for practical applications. In this study, we investigated the application of our previously proposed quantum pre-processing filter (QPF) to binary image classification. We evaluated the QPF on four datasets: MNIST (handwritten digits), EMNIST (handwritten digits and alphabets), CIFAR-10 (photographic images) and GTSRB (real-life traffic sign images). Similar to our previous multi-class classification results, the application of QPF improved the binary image classification accuracy using neural network against MNIST, EMNIST, and CIFAR-10 from 98.9% to 99.2%, 97.8% to 98.3%, and 71.2% to 76.1%, respectively, but degraded it against GTSRB from 93.5% to 92.0%. We then applied QPF in cases using a smaller number of training and testing samples, i.e. 80 and 20 samples per class, respectively. In order to derive statistically stable results, we conducted the experiment with 100 trials choosing randomly different training and testing samples and averaging the results. The result showed that the application of QPF did not improve the image classification accuracy against MNIST and EMNIST but improved it against CIFAR-10 and GTSRB from 65.8% to 67.2% and 90.5% to 91.8%, respectively. Further research will be conducted as part of future work to investigate the potential of QPF to assess the scalability of the proposed approach to larger and complex datasets.

翻译：过去几年，量子机器学习（QML）因其变革机器学习领域的潜力而受到研究者的广泛关注。目前已开发出多种利用量子力学特性的模型用于实际应用。本研究探讨了我们先前提出的量子预处理滤波器（QPF）在二元图像分类中的应用。我们在四个数据集上评估了QPF：MNIST（手写数字）、EMNIST（手写数字与字母）、CIFAR-10（摄影图像）和GTSRB（真实交通标志图像）。与我们之前的多类分类结果相似，应用QPF后，基于神经网络的二元图像分类准确率在MNIST、EMNIST和CIFAR-10上分别从98.9%提升至99.2%、97.8%提升至98.3%、71.2%提升至76.1%，但在GTSRB上从93.5%下降至92.0%。随后，我们在使用较少训练和测试样本（即每类分别使用80个训练样本和20个测试样本）的情况下应用了QPF。为获得统计稳定的结果，我们进行了100次实验，每次随机选择不同的训练和测试样本，并对结果取平均值。结果表明，应用QPF并未提升MNIST和EMNIST的图像分类准确率，但将CIFAR-10和GTSRB的准确率分别从65.8%提升至67.2%、90.5%提升至91.8%。未来工作将进一步研究QPF的潜力，以评估所提方法在更大规模复杂数据集上的可扩展性。