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arXiv:2509.13353

Hybrid Quantum-Classical Model for Image Classification

Muhammad Adnan Shahzad

Published Sep 14, 2025• 0 citations
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Abstract

This study presents a systematic comparison between hybrid quantum-classical neural networks and purely classical models across three benchmark datasets (MNIST, CIFAR100, and STL10) to evaluate their performance, efficiency, and robustness. The hybrid models integrate parameterized quantum circuits with classical deep learning architectures, while the classical counterparts use conventional convolutional neural networks (CNNs). Experiments were conducted over 50 training epochs for each dataset, with evaluations on validation accuracy, test accuracy, training time, computational resource usage, and adversarial robustness (tested with $ε=0.1$ perturbations).Key findings demonstrate that hybrid models consistently outperform classical models in final accuracy, achieving {99.38\% (MNIST), 41.69\% (CIFAR100), and 74.05\% (STL10) validation accuracy, compared to classical benchmarks of 98.21\%, 32.25\%, and 63.76\%, respectively. Notably, the hybrid advantage scales with dataset complexity, showing the most significant gains on CIFAR100 (+9.44\%) and STL10 (+10.29\%). Hybrid models also train 5--12$\times$ faster (e.g., 21.23s vs. 108.44s per epoch on MNIST) and use 6--32\% fewer parameters} while maintaining superior generalization to unseen test data.Adversarial robustness tests reveal that hybrid models are significantly more resilient on simpler datasets (e.g., 45.27\% robust accuracy on MNIST vs. 10.80\% for classical) but show comparable fragility on complex datasets like CIFAR100 ($\sim$1\% robustness for both). Resource efficiency analyses indicate that hybrid models consume less memory (4--5GB vs. 5--6GB for classical) and lower CPU utilization (9.5\% vs. 23.2\% on average).These results suggest that hybrid quantum-classical architectures offer compelling advantages in accuracy, training efficiency, and parameter scalability, particularly for complex vision tasks.

DOI
10.48550/arxiv.2509.13353

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This project compares classical, quantum, and hybrid neural network architectures for multiclass classification on the MNIST and CIFAR10 datasets. The models include a classical convolutional network, two pure quantum neural networks constructed with TorchQuantum, a quanvolutional model that applied fixed random quantum filters to image patches, and hybrid quantum classical networks that combine classical convolution with trainable quantum layers. In addition, a quantum classical quantum convolutional neural network (QCQ-CNN) inspired by recent works is implemented, as well as a separate Qiskit based QCQ-CNN that uses the ZZFeatureMap for data encoding and the RealAmplitudes ansatz for variational processing. Most models were trained under shared hyperparameters and full multiclass supervision, while the Qiskit based QCQ-CNN was trained on a reduced MNIST and CIFAR10 subset in a One-vs-All setting due to the computational cost of simulating quantum circuits. Across both datasets, the classical convolutional neural network achieved the highest accuracy. The pure quantum models performed well and showed small gaps between training and testing accuracy, which suggests that the models did not overfit and were stable. The quanvolutional model produced expressive features but heavily overfit. The TorchQuantum QCQ-CNN achieved lower accuracy than the classical baseline but showed a consistent generalization and avoided the overfitting seen in the quanvolutional mode. The One-vs-All v QCQ-CNN had a strong accuracy for each class, showing that hybrid neural networks can perform well when the task is reduced into smaller subproblems. These results highlight the strengths and weaknesses of current quantum and hybrid neural networks in practical scenarios. The experiments illustrate that shallow quantum circuits can be integrated into classical neural networks to assist in their generalization ability even while the quantum aspects are simulated.
Image classification is a fundamental task in deep learning, and recent advances in quantum computing have generated significant interest in quantum neural networks. Traditionally, Convolutional Neural Networks (CNNs) are employed to extract image features, while Multilayer Perceptrons (MLPs) handle decision making. However, parameterized quantum circuits offer the potential to capture complex image features and define sophisticated decision boundaries. In this paper, we present a novel Hybrid Quantum–Classical Neural Network (H-QNN) for image classification, and demonstrate its effectiveness using the MNIST dataset. Our model combines quantum computing with classical supervised learning to enhance classification accuracy and computational efficiency. In this study, we detail the architecture of the H-QNN, emphasizing its capability in feature learning and image classification. Experimental results demonstrate that the proposed H-QNN model outperforms conventional deep learning methods in various training scenarios, showcasing its effectiveness in high-dimensional image classification tasks. Additionally, we explore the broader applicability of hybrid quantum–classical approaches in other domains. Our findings contribute to the growing body of work in quantum machine learning, and underscore the potential of quantum-enhanced models for image recognition and classification.
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