We present a hybrid deepfake detection model that combines classical and quantum techniques for robust feature extraction. Our pipeline integrates an Xception-based CNN with a ViT to capture spatial features. A custom Quantum Convolutional Layer, implemented using PennyLane, is introduced to leverage quantum properties for enhanced feature representation. To address class imbalance and improve generalization, we employ focal loss, mixup augmentation, and test-time augmentation. Evaluated on the Deepfake and real images dataset, our model achieves a ROC-AUC of 0.995, with 0.984 precision and 0.982 F1-score. These results demonstrate the potential of hybrid quantum-classical architectures in advancing deepfake detection and open pathways for applying quantum methods to broader computer vision challenges.
We present a hybrid deepfake detection model that combines classical and quantum techniques for robust feature extraction. Our pipeline integrates an Xception-based CNN with a ViT to capture spatial features. A custom Quantum Convolutional Layer, implemented using PennyLane, is introduced to leverage quantum properties for enhanced feature representation. To address class imbalance and improve generalization, we employ focal loss, mixup augmentation, and test-time augmentation. Evaluated on the Deepfake and real images dataset, our model achieves a ROC-AUC of 0.995, with 0.984 precision and 0.982 F1-score. These results demonstrate the potential of hybrid quantum-classical architectures in advancing deepfake detection and open pathways for applying quantum methods to broader computer vision challenges.
The rise of deepfake technologies has posed significant challenges to privacy, security, and information integrity, particularly in audio and multimedia content. This paper introduces a Quantum-Trained Convolutional Neural Network (QT-CNN) framework designed to enhance the detection of deepfake audio, leveraging the computational power of quantum machine learning (QML). The QT-CNN employs a hybrid quantum-classical approach, integrating Quantum Neural Networks (QNNs) with classical neural architectures to optimize training efficiency while reducing the number of trainable parameters. Our method incorporates a novel quantum-to-classical parameter mapping that effectively utilizes quantum states to enhance the expressive power of the model, achieving up to 70% parameter reduction compared to classical models without compromising accuracy. Data pre-processing involved extracting essential audio features, label encoding, feature scaling, and constructing sequential datasets for robust model evaluation. Experimental results demonstrate that the QT-CNN achieves comparable performance to traditional CNNs, maintaining high accuracy during training and testing phases across varying configurations of QNN blocks. The QT framework’s ability to reduce computational overhead while maintaining performance underscores its potential for real-world applications in deepfake detection and other resource-constrained scenarios. This work highlights the practical benefits of integrating quantum computing into artificial intelligence, offering a scalable and efficient approach to advancing deepfake detection technologies.
Quantum machine learning has emerged as a promising approach for enhancing classical deep learning models by leveraging quantum computational properties.In this work, a hybrid Quantum-Classical Convolutional Neural Network (QC-CNN) is proposed for overhead image classification using the Overhead-MNIST dataset.The model integrates a classical Convolutional Neural Network (CNN) for spatial feature extraction with a parameterized quantum circuit that performs quantum feature transformation through angle embedding and strongly entangling layers.Initially, input images are preprocessed and resized, after which the CNN extracts high-level feature representations.These features are then encoded into quantum states and processed through a variational quantum layer to capture complex feature correlations using quantum operations.The output of the quantum circuit is passed to fully connected layers for final classification.The hybrid architecture is trained end-to-end using a gradient-based optimization approach.Experimental results demonstrate that the proposed QC-CNN model achieves high classification accuracy on the selected classes of the Overhead-MNIST dataset while maintaining stable training behavior.The results indicate that integrating quantum circuits with classical deep learning frameworks can improve feature representation and provide a potential pathway toward future quantum-enhanced intelligent vision systems.
With millions of medical data being produced daily, it is important to leverage current technology to improve the healthcare system. As traditional deep learning models are unable to effectively handle high-dimensional data, a hybrid classical-quantum approach is used to enhance traditional deep learning algorithms. The hybrid classical-quantum models are built upon the classical deep learning framework with quantum techniques. The classical layers are utilized for feature extraction while the quantum layer is used for computational tasks. This addresses computational limitations and data imbalance challenges in medical image analysis. Transfer learning models, such as ResNet18 and InceptionV3, are used in conjunction with a quantum variational circuit to perform the task. The model is trained on a chest X-ray dataset, which consists of two sets of data: binary and multiclass. The binary dataset consists of Normal and COVID-19 images, while the multiclass dataset has COVID-19, Normal, and Tuberculosis images for classification. PennyLane, as a simulator, has been used to perform quantum computing. The dataset has also been used to train the traditional transfer learning models, pre-trained ResNet18, and their results have been compared. Out of all the models tested, the hybrid classical-quantum model, namely Hybrid ResNet18, performed the best. It achieved an accuracy of 93.22% for the binary classification task and 93.91% for the multiclass classification. The results show that there is a statistically significant improvement in classification accuracy and F1-score of the hybrid approach over the classical transfer learning baselines under a simulation-based environment. This sets the stage for further research on real-world physical quantum hardware. Class imbalance and overlapping predictions have been addressed using methods such as data augmentation and confusion matrix analysis. Thus, quantum machine learning could be used as a vital tool to analyze medical data for better diagnostic results.
Brain tumor detection is a vital and challenging task in medical diagnosis, requiring highly precise and efficient methods to improve patient treatment outcomes. Traditional approaches rely on classical machine learning and deep learning techniques, which, while effective to a degree, often struggle with capturing the complex and high-dimensional patterns present in medical imaging data. A significant limitation of these conventional methods is their inability to process and analyze intricate features efficiently, which can impact diagnostic accuracy. To address these challenges, this work proposes a novel Quantum Convolutional Neural Network (QCNN) framework that combines quantum computing principles, via quantum circuits, with classical machine learning architectures implemented through tools like PyTorch and PennyLane. This hybrid approach enhances feature extraction and classification capabilities, leading to superior performance. Experimental results demonstrate that the QCNN model achieves an improved accuracy of 90 percent on the test dataset, surpassing traditional methods and showing promise for advancing brain tumor detection technology. This improvement is supported by consistent decreases in training and validation loss during model training, highlighting the robustness and efficacy of the quantum-enhanced model.
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 emergence of deep vision models such as convolutional neural networks and vision transformers has revolutionized computer vision, enabling significant advancements in image classification, object detection, and segmentation. In parallel, the rapid development of quantum computing has spurred interest in quantum machine learning (QML), which integrates the strengths of quantum computation with the representational power of deep learning. In QML, parameterized quantum circuits offer the potential to capture complex image features, define complex decision boundaries, and provide other computational advantages. This paper investigates hybrid quantum-classical vision architectures, with a focus on hybrid quantum-classical convolutional neural networks and hybrid quantum-classical vision transformers. These hybrid models explore both quantum pre-processing and post-processing of data, respectively, where quantum circuits are strategically integrated into the data pipeline to enhance model performance. Our results suggest that these hybrid models can enhance accuracy and computational efficiency in vision-related tasks, even with the constraints of current noisy intermediate-scale quantum devices.