A Scalable Quantum Non-local Neural Network for Image Classification

• URL: http://arxiv.org/abs/2407.18906v2
• Date: Thu, 22 Aug 2024 02:22:05 GMT
• Title: A Scalable Quantum Non-local Neural Network for Image Classification
• Authors: Sparsh Gupta, Debanjan Konar, Vaneet Aggarwal,
• Abstract summary: This article introduces a hybrid quantum-classical scalable non-local neural network, referred to as Quantum Non-Local Neural Network (QNL-Net) The proposed QNL-Net relies on inherent quantum parallelism to allow the simultaneous processing of a large number of input features. We benchmark our proposed QNL-Net with other quantum counterparts to binary classification with datasets MNIST and CIFAR-10.
• Score: 31.58287931295479
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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