Hybrid Quantum-Classical Model for Image Classification
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.
68cb6bfd5a7803ff3be42ddd
Muhammad Adnan Shahzad
claimed_verified
2025-09-18T13:26:45.211Z
68cb6bb026c197a95a009a60
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Ko-OoQ4wDOCvrqO22J4uu.png
Muhammad Adnan Shahzad
user
adnanphp
Hybrid Quantum-Classical Model for Image Classification
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.
Hybrid quantum-classical neural networks outperform classical models in accuracy, training efficiency, and parameter scalability across various datasets, especially for complex vision tasks.
hybrid quantum-classical neural networks parameterized quantum circuits classical deep learning architectures convolutional neural networks validation accuracy test accuracy training time computational resource usage adversarial robustness MNIST CIFAR100 STL10