Authors: Sparsh Gupta, Debanjan Konar, Vaneet Aggarwal
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.
Source: http://arxiv.org/abs/2407.18906v1
