A Scalable Quantum Non-local Neural Network for Image Classification
This work addresses scalability issues in non-local neural networks for computer vision researchers, though it is incremental as it builds on existing quantum and non-local methods.
The paper tackles the computational and memory challenges of scaling non-local neural networks for image classification by introducing a hybrid quantum-classical Quantum Non-Local Neural Network (QNL-Net), which achieves state-of-the-art accuracy in binary classification on MNIST and CIFAR-10 datasets while using fewer qubits.
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.