Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum-classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.

翻译：图像分类作为多个行业中的关键任务，面临着视觉数据量激增带来的计算挑战。本研究通过引入两种基于量子力学原理实现高效计算的量子机器学习模型来应对这些挑战。我们的第一个模型是一种带有并行量子电路的混合量子神经网络，即便在当前噪声中等规模量子时代（其中包含大量量子比特的电路尚不可行）也能执行计算。该模型在完整MNIST数据集上实现了创纪录的99.21%分类准确率，超越了已知的量子-经典混合模型性能，同时参数量仅为经典对应模型的八分之一。此外，该混合模型在医学MNIST数据集（分类准确率超过99%）和CIFAR-10数据集（分类准确率超过82%）上的测试结果，可作为模型泛化能力的证据，并凸显了量子层在区分输入数据共性特征方面的效率。我们的第二个模型引入了带有量子卷积层的混合量子神经网络，通过卷积过程降低图像分辨率。该模型在性能上与经典对应模型相当，但可训练参数仅为经典模型的四分之一，且优于同等参数量的经典模型。这些模型代表了量子机器学习研究的进展，为构建更精确的图像分类系统指明了方向。