Abstract High-dimensional classification remains challenging for hybrid quantum–classical learning because conventional variational quantum feature learning methods often assign equal importance to all input features and do not explicitly account for feature uncertainty. This paper presents Uncertainty-Aware Variational Quantum Feature Learning (UVQFL) , a hybrid framework that combines adaptive feature weighting, uncertainty estimation, and variational quantum representation learning for reliable classification. The proposed framework incorporates an Adaptive Feature Confidence Module (AFCM) to estimate the contribution of individual input features before quantum encoding and an Uncertainty Estimation Module (UEM) to characterize feature reliability during the learning process. The confidence-weighted features are encoded using a variational quantum circuit and subsequently processed by an enhanced multi-layer classical classifier. Benchmark datasets, including MNIST, Fashion-MNIST, and PathMNIST, are employed during data preprocessing and quantum feature generation to establish the proposed framework. The detailed experimental evaluation, performance analysis, and comparative results presented in this study are conducted on the MNIST benchmark dataset to provide a comprehensive assessment of the proposed architecture. A systematic investigation using 4-, 6-, and 8-qubit hybrid models examines the influence of quantum feature dimensionality on classification performance. The final UVQFL model achieves a best validation accuracy of 84.62% , with 84.60% precision , 84.62% recall , and an 84.59% F1-score . The results indicate that integrating adaptive feature confidence with uncertainty-aware quantum feature learning improves feature representation quality and enhances the reliability of hybrid quantum–classical classification.
Abstract High-dimensional classification remains challenging for hybrid quantum–classical learning because conventional variational quantum feature learning methods often assign equal importance to all input features and do not explicitly account for feature uncertainty. This paper presents Uncertainty-Aware Variational Quantum Feature Learning (UVQFL) , a hybrid framework that combines adaptive feature weighting, uncertainty estimation, and variational quantum representation learning for reliable classification. The proposed framework incorporates an Adaptive Feature Confidence Module (AFCM) to estimate the contribution of individual input features before quantum encoding and an Uncertainty Estimation Module (UEM) to characterize feature reliability during the learning process. The confidence-weighted features are encoded using a variational quantum circuit and subsequently processed by an enhanced multi-layer classical classifier. Benchmark datasets, including MNIST, Fashion-MNIST, and PathMNIST, are employed during data preprocessing and quantum feature generation to establish the proposed framework. The detailed experimental evaluation, performance analysis, and comparative results presented in this study are conducted on the MNIST benchmark dataset to provide a comprehensive assessment of the proposed architecture. A systematic investigation using 4-, 6-, and 8-qubit hybrid models examines the influence of quantum feature dimensionality on classification performance. The final UVQFL model achieves a best validation accuracy of 84.62% , with 84.60% precision , 84.62% recall , and an 84.59% F1-score . The results indicate that integrating adaptive feature confidence with uncertainty-aware quantum feature learning improves feature representation quality and enhances the reliability of hybrid quantum–classical classification.
Abstract Quantum Machine Learning (QML) has emerged as a promising paradigm that leverages the principles of quantum mechanics—such as superposition and entanglement—to overcome the limitations of classical machine learning in handling complex, high-dimensional data. However, practical challenges such as limited coherence times of qubits and the integration of quantum circuits with classical architectures have hindered the full realization of QML’s potential. To address these challenges, this paper proposes a novel hybrid architecture, termed the Classical-Quantum Hybrid Convolutional Neural Network (CQHCNN), which integrates quantum-inspired layers into conventional deep learning frameworks. These quantum layers utilize quantum information processing to extract complex features, while the classical components perform feature extraction and classification. This paper evaluates the performance of our model on several benchmark datasets, including MNIST, Fashion-MNIST, CIFAR-10 and COVID-19 Radiography, demonstrating its ability to outperform some other Quantum Convolutional Neural Networks (QCNNs) in terms of many metrics. Experimental results indicate that the hybrid model achieves significant improvements in classification accuracy and computational efficiency, highlighting the potential of quantum-inspired techniques to enhance classical machine learning models.
Medical image classification is an important component in computer-aided diagnosis and currently popular deep learning models are found to be inefficient in capturing the complex spatial relationships between the medical images while performing efficiently and generalizing well for multi-class classification.In this work, we introduce a novel Adaptive Variational Quantum Convolutional Neural Network (AVQCNN) based on quantum feature encoding and adaptive variational quantum convolution, multi-scale entanglement fusion, hybrid quantum-classical optimization, and uncertainty-aware prediction to boost the diagnostic accuracy.It has been successfully applied to PennyLane and TensorFlow, and evaluated using the MedMNIST v2 multi-class medical imaging benchmark with standard metrics.Implements and tests this framework with PennyLane and TensorFlow and uses MedMNIST v2 benchmark for medical imaging with standard performance metrics.Through experiments, an accuracy of 98.21%,precision of 97.94%,recall of 97.73%,F1 score of 97.83% and an AUC of 0.996 were achieved with training time of only 82 minutes, inference time of 9.4ms/image, 6.3 million parameters and 5.4GB of GPU memory.The results demonstrate its potential to provide a scalable and reliable quantum-enhanced framework for next-generation intelligent clinical decision support system, with its ability to boost both the classification accuracy and computation efficiency.
Hybrid quantum-classical neural networks have emerged as a promising approach for leveraging quantum computing in machine learning while mitigating current hardware limitations. This paper presents Sim-HVQC, a hybrid Deep Quantum Neural Network that couples an adaptive, parameter-free SimAM weighting module with classical feature extraction to preserve class-discriminative information prior to encoding into a Variational Quantum Circuit (VQC). Previous studies are restricted to binary classification [1] [2] [3] [4] [5]. In contrast, the proposed framework is trained and evaluated on various multi-class datasets(MNIST, KMNIST, Fashion-MNIST, and EMNIST). The framework further demonstrates reproducibility, parameter efficiency, and interpretability through multi-seed evaluation, parameter analysis, and latent/quantum feature inspection. The source code is publicly available at https://github.com/Dilli822/ SimAM-HVQC
Quantum machine learning has emerged as a promising research area for learning complex data patterns. However, most existing quantum feature maps employ fixed encoding strategies that do not explicitly consider the relationships among features within a dataset. In this study, we propose a Correlation Aware Quantum Feature Map (CAQFM) which integrates feature dependencies into the quantum encoding process. The proposed approach utilizes Pearson, Spearman, Kendall Tau, Mutual Information, and Distance Correlation measures to identify relationships among features. Dependencies exceeding a predefined threshold are incorporated into the quantum circuit through controlled quantum gates, enabling the construction of richer quantum representations that better reflect the underlying structure of the data.
The proposed method is evaluated using a Variational Quantum Classifier (VQC) on three benchmark datasets, namely breast cancer diagnosis, credit default prediction, and student placement classification. Simulation results demonstrate that correlation based quantum encoding can improve classification performance compared to conventional encoding strategies. In particular, the Spearman and Kendall Tau based CAQFM variants achieved the highest predictive performance and consistently outperformed standard quantum feature maps. The findings indicate that incorporating dependency information from classical data into quantum feature maps facilitates the generation of more discriminative quantum representations and enhances the effectiveness of variational quantum classifiers.
Achieving reliable radiological image classification requires not only high accuracy, but also well-calibrated uncertainty estimates, robustness in the face of domain shifts, and generalizability across various imaging modalities. Current deep learning methodologies for medical image classification frequently display overconfident predictions and experience significant degradation under realistic clinical conditions, such as label noise, limited training data, and distribution shifts between institutions. Hybrid quantum–classical machine learning has emerged as a promising paradigm; however, existing studies in medical imaging predominantly focus on single-modality and accuracy-centric approaches. In this study, we introduced Q-CaMIR (Cross-Modal, Uncertainty-Aware Hybrid Quantum–Classical Learning), a novel framework that integrates chest X-ray and brain MRI classification within a unified hybrid quantum–classical architecture, incorporating explicit uncertainty quantification. Q-CaMIR utilizes a pretrained classical vision backbone to extract modality-agnostic latent representations, aligns cross-modal features through a contrastive representation module, and channels the resulting compact embeddings through a variational quantum circuit (VQC) decision head, enhanced with evidential deep learning for uncertainty-aware prediction. We evaluated Q-CaMIR on three public benchmarks: CheXpert and MIMIC-CXR (external validation) for chest radiograph classification, and BraTS 2021 for brain tumor classification, employing a comprehensive suite of metrics including accuracy, macro-F1, AUROC, AUPRC, expected calibration error (ECE), and Brier score. Extensive experiments under low-data regimes, synthetic label noise, intensity corruption, and cross-institutional domain shifts demonstrate that Q-CaMIR achieves competitive classification performance compared to robust classical baselines while significantly improving calibration (up to 47% ECE reduction) and robustness. Ablation studies confirm the complementary contributions of the quantum decision head and uncertainty-aware training objective. Our findings suggest that hybrid quantum–classical learning provides a meaningful inductive bias for uncertainty-sensitive radiological classification, advancing towards clinically deployable AI systems for radiation-related diagnostic workflows.
Abstract Quantum Machine Learning (QML) has emerged as a promising research area for learning complex data patterns. However, most existing quantum feature maps employ fixed encoding strategies that do not explicitly consider the relationships among features within a dataset. In this study, we propose a Correlation Aware Quantum Feature Map (CAQFM) which integrates feature dependencies into the quantum encoding process. The proposed approach utilizes Pearson, Spearman, Kendall Tau, Mutual Information, and Distance Correlation measures to identify relationships among features. Dependencies exceeding a predefined threshold are incorporated into the quantum circuit through controlled quantum gates, enabling the construction of richer quantum representations that better reflect the underlying structure of the data. The proposed method is evaluated using a VQC on three benchmark datasets, namely breast cancer diagnosis, credit default prediction, and student placement classification. Simulation results demonstrate that correlation based quantum encoding can improve classification performance compared to conventional encoding strategies. In particular, the Spearman and Kendall Tau based CAQFM variants achieved the highest predictive performance and consistently outperformed standard quantum feature maps. The findings indicate that incorporating dependency information from classical data into quantum feature maps facilitates the generation of more discriminative quantum representations and enhances the effectiveness of variational quantum classifiers.