Dr. Harshavardhan Awari is a Senior Assistant Professor in the Department of Computer Science at VNR Vignana Jyothi Institute of Engineering & Technology (VNRVJIET), Hyderabad. He holds a Ph.D. in Computer Science and Engineering from JNTUH and has over 19 years of teaching experience. His research interests include Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Image Processing, and IoT, particularly their applications in healthcare. He has published extensively in SCI- and Scopus-indexed journals and international conferences and has served as a reviewer, session chair, technical committee member, and editorial member. He is the author of Hands-On Machine Learning: A Practical Guide and has guided Ph.D., M.Tech., and B.Tech. research projects.

Accurate classification of glioma grades from magnetic resonance imaging (MRI) is essential for clinical decision-making in neuro-oncology. Although deep learning performance has been impressive with classical models, they struggle with high dimensional medical imaging data and generalise poorly beyond their training data, especially in time- and resource-constrained settings. In light of the aforementioned challenges, we propose QuantumMedDx, a hybrid quantum–classical learning framework for classifying gliomas using MRI. The framework combines quantum feature encoding and variational quantum circuits with classical neural inference to improve diagnostic performance. The base model, QImageNet, uses amplitude-based quantum encoding for writing, entanglement-enabled parameterised quantum circuits (EPQCs) as feature extractors, and classical dense layers for classifying HGG and LGG from multimodal MRI slices. We demonstrate the effectiveness of the proposed approach on the BraTS 2021 benchmark dataset using a patient-aware 5-fold cross-validation protocol. Experimental results show that QuantumMedDx achieves accuracies of 94.12%, 93.30%, and 96.42%; F1-scores of 93.30% and 96.42%; and AUCs of 96.42% and 96.42%, respectively, outperforming conventional CNN, DNN, and SVM baselines. Ablation studies provide additional evidence of the performance improvements enabled by quantum Fourier transform and entanglement layers. Such results suggest that quantum–classical learning can efficiently improve feature extraction and discrimination in medical imaging, thus providing a modular and scalable route towards quantum–inspired clinical decision support systems of the future.