Open Access
LEVERAGING QUANTUM CONVOLUTIONAL LAYERS FOR ENHANCED IMAGE CLASSIFICATION: AN EXAMINATION OF QUANVOLUTIONAL NEURAL NETWORK CHARACTERISTICS
4
Department of Computer Engineering, Politecnico di Milano, Italy
4
Department of Computer Engineering, Politecnico di Milano, Italy
Abstract
The rapid advancements in quantum computing have opened new avenues for enhancing classical machine learning paradigms, particularly in the realm of image classification. Traditional Deep Convolutional Neural Networks (DCNNs) have achieved remarkable success, yet they face challenges related to computational intensity and the need for vast datasets [4, 19, 31]. Quanvolutional Neural Networks (QNNs) emerge as a promising hybrid quantum-classical approach that integrates quantum circuits directly into the feature extraction process of convolutional layers. This article explores the fundamental characteristics and operational advantages of QNNs, focusing on how their unique quantum-enhanced feature maps contribute to improved image classification performance. We delve into the architecture of quanvolutional layers, the mechanisms of data encoding, and the potential for quantum advantage in feature learning. By synthesizing recent research, we demonstrate the theoretical underpinnings and observed benefits of QNNs in extracting richer, more discriminative features, potentially leading to higher accuracy and efficiency, especially in the Noisy Intermediate-Scale Quantum (NISQ) era [22]. Challenges such as data encoding complexity, parameter optimization, and hardware limitations are also discussed, alongside future directions for scalable and robust QNN implementations.
Keywords
Quantum convolutional layers,
quanvolutional neural networks,
quantum machine learning,
image classification
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. https://doi.org/10.1038/s43588-021-00084-1
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. https://doi.org/10.1007/s42484-021-00056-8
Araujo IF, Park DK, Petruccione F, da Silva AJ (2021) A divide-and-conquer algorithm for quantum state preparation. Sci Rep 11:6329. https://doi.org/10.1038/s41598-021-85474-1
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. https://doi.org/10.1016/j.mlwa.2021.100134
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. https://doi.org/10.1103/PhysRevResearch.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. https://doi.org/10.1103/PhysRevA.78.052310
Giovannetti V, Lloyd S, Maccone L (2008b) Quantum random access memory. Phys Rev Lett 100:160501. https://doi.org/10.1103/PhysRevLett.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. https://doi.org/10.1007/s42484-020-00012-y
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. https://doi.org/10.1016/j.eswa.2022.116512
Hur T, Kim L, Park DK (2022) Quantum convolutional neural network for classical data classification. Quantum Mach Intell 4(1):3. https://doi.org/10.1007/s42484-021-00061-x
Jeswal SK, Chakraverty S (2019) Recent developments and applications in quantum neural network: a review. Arch Comput Methods Eng 26(4):793–807. https://doi.org/10.1007/s11831-018-9269-0
Kou C, Yang H (2023) A mini-batch stochastic conjugate gradient algorithm with variance reduction. J Glob Optim 87(2):1009–1025. https://doi.org/10.1007/s10898-022-01205-4
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. https://doi.org/10.1145/3065386
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791
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. https://doi.org/10.1109/TNNLS.2021.3084827
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. https://doi.org/10.1007/s11433-021-1734-3
Mattern D, Martyniuk D, Willems H, Bergmann F, Paschke A (2021) Variational quanvolutional neural networks with enhanced image encoding. Preprint at arXiv:2106.07327
Preskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79. https://doi.org/10.22331/q-2018-08-06-79
Schuld M, Killoran N (2022) Is quantum advantage the right goal for quantum machine learning? PRX Quantum 3:030101. https://doi.org/10.1103/PRXQuantum.3.010101
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
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. https://doi.org/10.1002/qute.201900070
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. https://doi.org/10.1016/j.jare.2021.03.015
Verdon G, McCourt T, Luzhnica E, Singh V, Leichenauer S, Hidary J (2019) Quantum graph neural networks. Preprint at arXiv:1909.12264
Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci 2018:7068349. https://doi.org/10.1155/2018/7068349
Wei S, Chen Y, Zhou Z, Long G (2022) A quantum convolutional neural network on NISQ devices. AAPPS Bulletin 32(1):2. https://doi.org/10.1007/s43673-021-00030-3
Weigold M, Barzen J, Leymann F, Salm M (2022) Data encoding patterns for quantum computing. Proceedings of the 27th conference on pattern languages of programs. USA, The Hillside Group.
Wierichs D, Izaac J, Wang C, Lin CY-Y (2022) General parameter-shift rules for quantum gradients. Quantum 6:677. https://doi.org/10.22331/q-2022-03-30-677
Wu J, Li Q (2022) Poster: scalable quantum convolutional neural networks for edge computing. Unpublished.
Xiao H, Rasul K, Vollgraf R (2017). Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. Preprint at arXiv:1708.07747
Zhang S-X, Hsieh C-Y, Zhang S, Yao H (2022) Differentiable quantum architecture search. Quantum Sci Technol 7(4):045023
Zoufal C, Lucchi A, Woerner S (2019) Quantum generative adversarial networks for learning and loading random distributions. Quantum Information 5(1):103. https://doi.org/10.1038/s41534-019-0223-2
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