A Scalable Quantum Non-local Neural Network for Image Classification
<|reference_start|>A Scalable Quantum Non-local Neural Network for Image Classification: 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.<|reference_end|>
@article{gupta2024a,
    title={A Scalable Quantum Non-local Neural Network for Image Classification},
    author={Sparsh Gupta and Debanjan Konar and Vaneet Aggarwal},
    journal={arXiv preprint arXiv:2407.18906},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18906},
    primaryClass={cs.CV cs.AI cs.IT cs.LG math.IT quant-ph}
}