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International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
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A Hybrid Quantum–Classical Deep Learning Approach for Image Recognition: Performance Analysis of Quanvolution-Based Convolutional Models

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Dr. James William Carter 1 , Dr. Emily Rose Thompson 1 ,
4 School of Engineering, University of Manchester, Manchester, UK
4 Department of Computer Science, University of Oxford, Oxford, UK

Abstract

The integration of quantum computing principles with classical deep learning architectures has emerged as a promising direction for advancing image recognition systems beyond the limitations of purely classical convolutional neural networks (CNNs). This study proposes and analyzes a hybrid quantum–classical deep learning framework based on quanvolutional neural network (QNN) principles, where quantum circuits are embedded within convolutional feature extraction layers to enhance representation capability. The research investigates how quantum-enhanced filters, particularly quanvolutional layers, contribute to improved feature diversity, non-linear transformation capacity, and classification accuracy in image recognition tasks.

Building upon established convolutional architectures and quantum machine learning theories, the proposed framework integrates quantum random circuit encoding with classical deep neural networks to evaluate performance gains in comparison to conventional CNN models. The study further incorporates theoretical insights from hybrid quantum-classical learning systems, emphasizing the role of parameterized quantum circuits in feature mapping and optimization stability. The methodology is grounded in NISQ-era quantum computing constraints and leverages hybrid optimization strategies to ensure computational feasibility.

Experimental evaluation demonstrates that quanvolution-based models can provide improved robustness in feature extraction, particularly in high-dimensional image spaces, while maintaining competitive computational efficiency. However, scalability limitations and noise sensitivity in quantum circuits remain key challenges.

Overall, this work contributes to the growing field of quantum-enhanced machine learning by providing a structured performance analysis of hybrid quanvolutional architectures and highlighting their potential role in next-generation intelligent visual recognition systems.

Keywords

Quantum Machine Learning, Quanvolutional Neural Networks,, Hybrid Deep Learning

References

Abbas A, Sutter D, Zoufal C, Lucchi A, Figalli A, Woerner S (2021) The power of quantum neural networks. Nat Comput Sci 1:403–409.
Alchieri L, Badalotti D, Bonardi P, Bianco S (2021) An introduction to quantum machine learning: from quantum logic to quantum deep learning. Quantum Mach Intell 3:28.
Araujo IF, Park DK, Petruccione F, da Silva AJ (2021) A divide-and-conquer algorithm for quantum state preparation. Sci Rep 11:6329.
Chai J, Zeng H, Li A, Ngai EW (2021) Deep learning in computer vision: a critical review of emerging techniques and application scenarios. Mach Learn Appl 6:100134.
Chen SY-C, Wei T-C, Zhang C, Yu H, Yoo S (2022) Quantum convolutional neural networks for high energy physics data analysis. Phys Rev Res 4:013231.
Duong T, Truong ST, Tam M, Bach B, Ryu J-Y, Rhee J-KK (2022) Quantum neural architecture search with quantum circuits metric and Bayesian optimization. Preprint at arXiv:2206.14115.
Giovannetti V, Lloyd S, Maccone L (2008a) Architectures for a quantum random access memory. Phys Rev A 78:052310.
Giovannetti V, Lloyd S, Maccone L (2008b) Quantum random access memory. Phys Rev Lett 100:160501.
Hai VT, Viet NT, Ho LB (2023) Variational preparation of entangled states on quantum computers. Preprint at arXiv:2306.17422.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Preprint at arXiv:1512.03385.
Henderson M, Shakya S, Pradhan S, Cook T (2020) Quanvolutional neural networks: powering image recognition with quantum circuits. Quantum Machine Intelligence 2(1):2.
Houssein EH, Abohashima Z, Elhoseny M, Mohamed WM (2022) Machine learning in the quantum realm: the state-of-the-art, challenges, and future vision. Expert Syst Appl 194:116512.
Hur T, Kim L, Park DK (2022) Quantum convolutional neural network for classical data classification. Quantum Mach Intell 4(1):3.
Jeswal SK, Chakraverty S (2019) Recent developments and applications in quantum neural network: a review. Arch Comput Methods Eng 26(4):793–807.
Kou C, Yang H (2023) A mini-batch stochastic conjugate gradient algorithm with variance reduction. J Glob Optim 87(2):1009–1025.
Krizhevsky A, Hinton G et al (2009) Learning multiple layers of features from tiny images. Unpublished.
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90.
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324.
Li Z, Liu F, Yang W, Peng S, Zhou J (2022) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst 33(12):6999–7019.
Liu J, Lim KH, Wood KL, Huang W, Guo C, Huang H-L (2021) Hybrid quantum-classical convolutional neural networks. Sci China Phys Mech Astron 64(9):290311.
Mattern D, Martyniuk D, Willems H, Bergmann F, Paschke A (2021) Variational quanvolutional neural networks with enhanced image encoding.
Preskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79.
Schuld M, Petruccione F (2018) Supervised learning with quantum computers (Vol. 17). Springer.
Schuld M, Petruccione F (2021) Machine learning with quantum computers (No. 2). Springer.
Schuld M, Killoran N (2022) Is quantum advantage the right goal for quantum machine learning? PRX Quantum 3:030101.
Sim S, Johnson PD, Aspuru-Guzik A (2019) Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Adv Quantum Technol 2(12):1900070.
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Preprint at arXiv:1409.1556.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A (2015) Going deeper with convolutions. Preprint at arXiv:1409.4842.
Tulbure A-A, Tulbure A-A, Dulf E-H (2022) A review on modern defect detection models using DCNNs – deep convolutional neural networks. J Adv Res 35:33–48.

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