Hybrid Quantum-Classical Model for Image Classification
Muhammad Adnan Shahzad
2025-09-18
Summary
This research compares new hybrid quantum-classical neural networks to traditional, fully classical neural networks to see if combining quantum computing with standard machine learning can improve performance on image recognition tasks.
What's the problem?
Traditional neural networks, while powerful, can be slow to train, require a lot of computing power, and sometimes struggle with complex datasets. Researchers are exploring whether incorporating quantum computing could address these limitations and create more efficient and accurate models, but it hasn't been systematically tested against standard methods across different image datasets.
What's the solution?
The researchers built neural networks that combine classical computer processing with quantum circuits. They then trained both these hybrid models and standard classical neural networks (specifically, convolutional neural networks) on three different image datasets: MNIST (simple handwritten digits), CIFAR100 (more complex colored objects), and STL10 (even more complex images). They compared how accurately each type of network classified images, how long it took to train them, how much computing resources they used, and how well they held up against intentionally misleading images designed to trick the network.
Why it matters?
The results show that these hybrid quantum-classical networks often perform better than traditional networks, especially on more challenging image datasets. They also train faster and use fewer resources. This suggests that combining quantum computing with classical machine learning is a promising direction for building more powerful and efficient artificial intelligence, particularly for tasks like image recognition where current methods can be limited.
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 epsilon=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--12times 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 (sim1\% 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.