Table1_Performance.csv This file contains the classification performance of the Classical CNN and the proposed SH-QC-CNN across three benchmark datasets: ImageNet-1K, CIFAR-100, and Medical X-ray. It reports Top-1 accuracy for both models and the percentage improvement achieved by the hybrid architecture. The data supports the claim that quantum-enhanced feature processing consistently improves recognition accuracy across diverse domains. Fig7_Parameter_Counts.csv This dataset reports the number of trainable parameters (in millions) for Classical CNNs and SH-QC-CNN on three datasets. It demonstrates the substantial reduction in model size achieved by replacing dense classical layers with Parameterized Quantum Circuits (PQCs), highlighting the parameter efficiency of the proposed hybrid architecture. Fig8_Training_Time.csv This file provides the total training time (in hours) for Classical CNNs and SH-QC-CNN across ImageNet-1K, CIFAR-100, and Medical X-ray datasets. The data shows that SH-QC-CNN reduces training time by approximately 34% on average, validating the computational efficiency of quantum-enhanced feature compression. Fig9_PQC_Depth_vs_Accuracy.csv This dataset captures the relationship between PQC depth (number of quantum layers) and classification accuracy. It illustrates that performance improves with depth up to three layers, after which accuracy degrades due to NISQ-era limitations such as noise and barren plateaus. The data justifies the selection of a three-layer PQC as the optimal configuration. Fig11_Noise_Robustness.csv This file contains clean and noisy accuracy values for both Classical CNN and SH-QC-CNN under Gaussian noise perturbation. It quantifies the accuracy drop and demonstrates that SH-QC-CNN is significantly more robust to input noise, confirming the stabilizing effect of quantum feature embeddings. Fig12_Qubit_Scaling.csv This dataset shows the relationship between classical feature dimensionality and the number of qubits required for quantum encoding. It evidences a logarithmic scaling behavior, indicating that high-dimensional feature spaces can be efficiently represented using a small number of qubits, making the architecture feasible for NISQ-era hardware.
Table1_Performance.csv This file contains the classification performance of the Classical CNN and the proposed SH-QC-CNN across three benchmark datasets: ImageNet-1K, CIFAR-100, and Medical X-ray. It reports Top-1 accuracy for both models and the percentage improvement achieved by the hybrid architecture. The data supports the claim that quantum-enhanced feature processing consistently improves recognition accuracy across diverse domains. Fig7_Parameter_Counts.csv This dataset reports the number of trainable parameters (in millions) for Classical CNNs and SH-QC-CNN on three datasets. It demonstrates the substantial reduction in model size achieved by replacing dense classical layers with Parameterized Quantum Circuits (PQCs), highlighting the parameter efficiency of the proposed hybrid architecture. Fig8_Training_Time.csv This file provides the total training time (in hours) for Classical CNNs and SH-QC-CNN across ImageNet-1K, CIFAR-100, and Medical X-ray datasets. The data shows that SH-QC-CNN reduces training time by approximately 34% on average, validating the computational efficiency of quantum-enhanced feature compression. Fig9_PQC_Depth_vs_Accuracy.csv This dataset captures the relationship between PQC depth (number of quantum layers) and classification accuracy. It illustrates that performance improves with depth up to three layers, after which accuracy degrades due to NISQ-era limitations such as noise and barren plateaus. The data justifies the selection of a three-layer PQC as the optimal configuration. Fig11_Noise_Robustness.csv This file contains clean and noisy accuracy values for both Classical CNN and SH-QC-CNN under Gaussian noise perturbation. It quantifies the accuracy drop and demonstrates that SH-QC-CNN is significantly more robust to input noise, confirming the stabilizing effect of quantum feature embeddings. Fig12_Qubit_Scaling.csv This dataset shows the relationship between classical feature dimensionality and the number of qubits required for quantum encoding. It evidences a logarithmic scaling behavior, indicating that high-dimensional feature spaces can be efficiently represented using a small number of qubits, making the architecture feasible for NISQ-era hardware.
The objective of this research is to develop a Hybrid Quantum-Classical Convolutional Neural Network (QC-CNN) framework that integrates parameterized quantum circuits (PQCs) within classical CNN architectures to enhance image classification performance.The proposed model leverages quantum principles such as superposition and entanglement for high-dimensional feature representation, achieving superior accuracy and reduced computational complexity.Experiments on MNIST and CIFAR-10 datasets demonstrated improved classification accuracy of 98.7% and 82.5%, respectively, surpassing traditional CNNs while reducing training time by 33% and parameters by 45%.Statistical analysis confirmed the significance of these improvements.Visualizations using t-SNE revealed enhanced class separability, and noise perturbation tests validated the model's robustness.The results highlight the hybrid QC-CNN's potential for efficient and scalable quantum-enhanced deep learning applications.Extended evaluations, including quantum-layer-depth analysis, robustness testing, and t-SNE visualization, empirically support the hybrid QC-CNN's effectiveness across varied scenarios.
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.
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× 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.
Accurate and computationally efficient classification of brain tumors from MRI scans is critical for deployment in resource-constrained clinical settings, where conventional deep CNN architectures-despite achieving strong accuracy-often require large parameter counts that limit scalability. This work proposes a hybrid quantum-classical deep learning framework that combines a classical convolutional feature extractor with a variational quantum circuit (VQC) for multi-class tumor classification. Spatial features extracted by the CNN backbone will be dimensionality-reduced and encoded into quantum states using angle encoding; a parameterized quantum circuit will then process these features in a higher-dimensional Hilbert space, leveraging superposition and entanglement to capture non-linear feature correlations. Measurement outputs from the quantum layer will be passed to a classical fully connected layer for classification into glioma, meningioma, pituitary tumor, and no-tumor categories. The proposed framework will be evaluated on a publicly available benchmark MRI dataset and benchmarked against state-of-the-art classical CNN architectures in terms of accuracy, precision, recall, F1-score, and parameter efficiency. This study aims to demonstrate that quantum-enhanced layers can achieve competitive classification performance while substantially reducing trainable parameters, offering a practical direction for efficient, quantum-assisted medical image analysis.
Introduction: Medical image classification is a crucial task in cancer diagnosis, relying on the accurate analysis of high-dimensional imaging data. While Convolutional Neural Networks (CNNs) have shown great success in this domain, their performance is often limited by the shallow feature expressiveness and overfitting, particularly in small or heterogeneous datasets. Methods: Quantum machine learning offers new opportunities through high-dimensional representations and nonlinear transformations. In this work, we propose a Quantum-Enhanced Sandwich Convolutional Neural Network (QSCNN), a layered hybrid architecture that integrates quantum and classical modules. The model employs a quanvolutional layer for localized quantum feature extraction, followed by conventional convolution and pooling for hierarchical representation learning, and a variational quantum classifier for nonlinear decision-making. Results: QSCNN achieved higher accuracy and training stability than classical CNNs and QCCNN baselines across three medical imaging tasks.: brain tumor MRI, skin cancer dermoscopy, and lung cancer CT. Circuit depth analysis revealed a trade-off between expressiveness and robustness, and additional experiments with depolarizing noise confirmed the model’s resilience under realistic quantum error conditions. Discussion: This suggests that circuit design choices influence hybrid model behavior and generalization, supporting the feasibility of quantum-enhanced methods for small-sample medical imaging. However, the current evaluation is limited to relatively small benchmark datasets, and broader validation on large-scale data will be essential to confirm clinical applicability. Conclusion: In summary, QSCNN presents a feasible hybrid framework for enhancing medical image classification with quantum features. While preliminary, our results suggest potential advantages in accuracy and stability under NISQ conditions.
Quantum machine learning is emerging as a promising frontier at the intersection of quantum computing and Artificial Intelligence, offering potential gains in speed, efficiency, and robustness. In this paper, a hybrid Quantum Convolutional Neural Network (QCNN) architecture is presented for visual pattern recognition tasks, leveraging quantum superposition and entanglement for enhanced feature extraction. Unlike previous studies that focus on small-scale or simplified datasets, this study systematically evaluates QCNNs across multiple benchmark datasets and provides a direct comparison with classical CNNs, highlighting practical quantum advantages such as parameter efficiency and noise-resilience. The proposed approach encodes classical image data from benchmark datasets, including70K28×28grayscale images from MNIST and FashionMNIST, and60Kdownsampled grayscale CIFAR-10 images, into quantum states using amplitude encoding, followed by hierarchical quantum convolution and pooling layers implemented via parametrised quantum circuits (PQCs). The model culminates in a classical dense layer for final classification. Experimental results demonstrate that the QCNN achieves competitive performance on low-dimensional grayscale datasets with 93.2 % accuracy on MNIST and 88.5% on Fashion MNIST, though it underperforms on more complex datasets like CIFAR-10 with 67.8 % accuracy due to current quantum hardware limitations. While not yet surpassing classical CNNs in raw accuracy, the proposed QCNN exhibits notable parameter efficiency, noise resilience, and theoretical potential for energy-efficient computation, highlighting its promise for future applications as quantum technologies mature.