Accurate Image Multi-Class Classification Neural Network Model with Quantum Entanglement Approach
Article
Article Title | Accurate Image Multi-Class Classification Neural Network Model with Quantum Entanglement Approach |
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ERA Journal ID | 34304 |
Article Category | Article |
Authors | Riaz, Farina, Abdulla, Shahab, Suzuki, Hajime, Ganguly, Srinjoy, Deo, Ravinesh C. and Hopkins, Susan |
Journal Title | Sensors |
Journal Citation | 23 (5), pp. 1-11 |
Article Number | 2753 |
Number of Pages | 11 |
Year | 2023 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1424-8220 |
1424-8239 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s23052753 |
Web Address (URL) | https://www.mdpi.com/1424-8220/23/5/2753 |
Abstract | Quantum machine learning (QML) has attracted significant research attention over the last decade. Multiple models have been developed to demonstrate the practical applications of the quantum properties. In this study, we first demonstrate that the previously proposed quanvolutional neural network (QuanvNN) using a randomly generated quantum circuit improves the image classification accuracy of a fully connected neural network against the Modified National Institute of Standards and Technology (MNIST) dataset and the Canadian Institute for Advanced Research 10 class (CIFAR-10) dataset from 92.0% to 93.0% and from 30.5% to 34.9%, respectively. We then propose a new model referred to as a Neural Network with Quantum Entanglement (NNQE) using a strongly entangled quantum circuit combined with Hadamard gates. The new model further improves the image classification accuracy of MNIST and CIFAR-10 to 93.8% and 36.0%, respectively. Unlike other QML methods, the proposed method does not require optimization of the parameters inside the quantum circuits; hence, it requires only limited use of the quantum circuit. Given the small number of qubits and relatively shallow depth of the proposed quantum circuit, the proposed method is well suited for implementation in noisy intermediate-scale quantum computers. While promising results were obtained by the proposed method when applied to the MNIST and CIFAR-10 datasets, a test against a more complicated German Traffic Sign Recognition Benchmark (GTSRB) dataset degraded the image classification accuracy from 82.2% to 73.4%. The exact causes of the performance improvement and degradation are currently an open question, prompting further research on the understanding and design of suitable quantum circuits for image classification neural networks for colored and complex data. |
Keywords | artificial intelligence; artificial neural network; intelligent transportation system; quantum computer; quantum computing; quantum machine learning; traffic signs |
Related Output | |
Is part of | Coloured image classification with quantum machine learning algorithms for intelligent transportation systems |
ANZSRC Field of Research 2020 | 460201. Artificial life and complex adaptive systems |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | UniSQ College |
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
School of Mathematics, Physics and Computing | |
UniSQ College (Pathways) |
https://research.usq.edu.au/item/x2804/accurate-image-multi-class-classification-neural-network-model-with-quantum-entanglement-approach
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