Non-local operations play a crucial role in computer vision enabling the capture of long-range dependencies through weighted sums of features across the input, surpassing the constraints of traditional convolution operations that focus solely on local neighborhoods. Non-local operations typically require computing pairwise relationships between all elements in a set, leading to quadratic complexity in terms of time and memory. Due to the high computational and memory demands, scaling non-local neural networks to large-scale problems can be challenging. This article introduces a hybrid quantum-classical scalable non-local neural network, referred to as Quantum Non-Local Neural Network (QNL-Net), to enhance pattern recognition. The proposed QNL-Net relies on inherent quantum parallelism to allow the simultaneous processing of a large number of input features enabling more efficient computations in quantum-enhanced feature space and involving pairwise relationships through quantum entanglement. We benchmark our proposed QNL-Net with other quantum counterparts to binary classification with datasets MNIST and CIFAR-10. The simulation findings showcase our QNL-Net achieves cutting-edge accuracy levels in binary image classification among quantum classifiers while utilizing fewer qubits.

翻译：非局部操作在计算机视觉中扮演着关键角色，它通过加权求和输入中所有位置的特征来捕获长程依赖关系，从而超越了传统卷积操作仅关注局部邻域的局限。非局部操作通常需要计算集合中所有元素之间的两两关系，导致时间和内存复杂度呈二次方增长。由于高昂的计算和内存需求，将非局部神经网络扩展至大规模问题具有挑战性。本文提出了一种混合量子-经典的可扩展非局部神经网络，称为量子非局部神经网络（QNL-Net），以增强模式识别能力。所提出的QNL-Net利用固有的量子并行性，能够同时处理大量输入特征，从而在量子增强的特征空间中实现更高效的计算，并通过量子纠缠引入两两关系。我们使用MNIST和CIFAR-10数据集，将所提出的QNL-Net与其他量子模型在二分类任务上进行基准测试。仿真结果表明，在量子分类器中，我们的QNL-Net在二值图像分类上达到了前沿的准确率水平，同时使用了更少的量子比特。