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arXiv:2304.05830 · Physical Review Applied

Quantum-Enhanced Pattern Recognition

Giuseppe Ortolano, Carmine Napoli, Cillian Harney, Stefano Pirandola, Giuseppe Leonetti, Pauline Boucher, Elena Losero, Marco Genovese, Ivano Ruo Berchera

Published Aug 29, 2023• 26 citations
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Abstract

This work demonstrates experimentally that the advantage gained in sensing using quantum photonic resources can be sustained, and even amplified, through complex classical post-processing aimed at extracting relevant features. Despite the very different architectures of the classical algorithms tested here, the quantum advantage in classification performance appears to be robust and qualitatively very consistent. Thus the results argue for widespread use of quantum sensing technologies, in any field that deals with pattern recognition in large datasets.

DOI
10.1103/physrevapplied.20.024072

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This work demonstrates experimentally that the advantage gained in sensing using quantum photonic resources can be sustained, and even amplified, through complex classical post-processing aimed at extracting relevant features. Despite the very different architectures of the classical algorithms tested here, the quantum advantage in classification performance appears to be robust and qualitatively very consistent. Thus the results argue for widespread use of quantum sensing technologies, in any field that deals with pattern recognition in large datasets.
Quantum machine learning is emerging as a promising frontier at the intersection of quantum computing and Artificial Intelligence, offering potential gains in speed, efficiency, and robustness. In this paper, a hybrid Quantum Convolutional Neural Network (QCNN) architecture is presented for visual pattern recognition tasks, leveraging quantum superposition and entanglement for enhanced feature extraction. Unlike previous studies that focus on small-scale or simplified datasets, this study systematically evaluates QCNNs across multiple benchmark datasets and provides a direct comparison with classical CNNs, highlighting practical quantum advantages such as parameter efficiency and noise-resilience. The proposed approach encodes classical image data from benchmark datasets, including70 K28×2870 ~\mathrm{K} 28 \times 2870 K28×28grayscale images from MNIST and FashionMNIST, and60K\mathbf{6 0 K}60Kdownsampled grayscale CIFAR-10 images, into quantum states using amplitude encoding, followed by hierarchical quantum convolution and pooling layers implemented via parametrised quantum circuits (PQCs). The model culminates in a classical dense layer for final classification. Experimental results demonstrate that the QCNN achieves competitive performance on low-dimensional grayscale datasets with 93.2 % accuracy on MNIST and 88.5% on Fashion MNIST, though it underperforms on more complex datasets like CIFAR-10 with 67.8 % accuracy due to current quantum hardware limitations. While not yet surpassing classical CNNs in raw accuracy, the proposed QCNN exhibits notable parameter efficiency, noise resilience, and theoretical potential for energy-efficient computation, highlighting its promise for future applications as quantum technologies mature.
Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of binary photonic protocols, e.g., for target detection or memory cell readout. In this work, we generalize this theoretical model to the multipartite setting of barcode decoding and pattern recognition. We start by defining a digital image as an array or grid of pixels, each pixel corresponding to an ensemble of quantum channels. Specializing each pixel to a black and white alphabet, we naturally define an optical model of a barcode. In this scenario, we show that the use of quantum entangled sources, combined with suitable measurements and data processing, greatly outperforms classical coherent-state strategies for the tasks of barcode data decoding and classification of black and white patterns. Moreover, introducing relevant bounds, we show that the problem of pattern recognition is significantly simpler than barcode decoding, as long as the minimum Hamming distance between images from different classes is large enough. Finally, we theoretically demonstrate the advantage of using quantum sensors for pattern recognition with the nearest-neighbor classifier, a supervised learning algorithm, and numerically verify this prediction for handwritten digit classification.
Quantum-inspired neural networks represent a novel approach to pattern recognition, leveraging the principles of quantum mechanics to enhance the exploration of high-dimensional spaces and the detection of subtle correlations. This paper explores the integration of quantum-inspired concepts – specifically superposition and entanglement – into neural network architectures to address the limitations of current models in complex pattern identification. We investigate the impact of these design choices on network performance across diverse datasets, demonstrating potential improvements in accuracy and robustness. This work seeks to advance the field of neural network design by introducing a fundamentally new methodology for pattern recognition.
Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of binary photonic protocols, e.g., for target detection or memory cell readout. In this work, we generalize this theoretical model to the multi-partite setting of barcode decoding and pattern recognition. We start by defining a digital image as an array or grid of pixels, each pixel corresponding to an ensemble of quantum channels. Specializing each pixel to a black and white alphabet, we naturally define an optical model of barcode. In this scenario, we show that the use of quantum entangled sources, combined with suitable measurements and data processing, greatly outperforms classical coherent-state strategies for the tasks of barcode data decoding and classification of black and white patterns. Moreover, introducing relevant bounds, we show that the problem of pattern recognition is significantly simpler than barcode decoding, as long as the minimum Hamming distance between images from different classes is large enough. Finally, we theoretically demonstrate the advantage of using quantum sensors for pattern recognition with the nearest neighbor classifier, a supervised learning algorithm, and numerically verify this prediction for handwritten digit classification.