Enhanced deep learning and quantum variational classifier for large-scale data analysis

Abstract

Quantum machine learning (QML) is a method for analyzing vast volumes of health data, identifying possible higher-order interactions in medicine, and improving the accuracy of smart healthcare diagnosis and treatment. This paper presents a novel hybrid framework that integrates Inception-based Attentional VGG (IAV) with a Quantum Variational Classifier (QVC) and Parameterized Quantum Circuits (PQCs) for large-scale healthcare data analysis. Unlike existing models that face scalability, noise sensitivity, and high computational cost, the proposed approach combines deep learning feature extraction with quantum-enhanced classification to improve efficiency and accuracy. QML large-scale data are pre-processed with min-max normalization algorithms, which place feature values into a fixed range of uniformity and facilitate convergence learning. To extract features from pre-processed large-scale medical data analysis, Inception-based Attentional VGG is used. The quantum variational classifier is then utilized to categorize large-scale data in the classification method. Then, parameterized quantum circuits use a classical optimizer to get information about quantum measurements of parameters in tunable quantum functions. This model makes use of a dataset, namely the MIMIC-III clinical dataset, which is used to collect vast amounts of data for clinical health patients. The proposed model is then utilized to assess the performance of metrics like accuracy, precision, recall, and the F1 score. Experimental results show that the proposed approach achieves an accuracy of 98.76%, precision of 98.64%, recall of 98.12%, and F1-score of 98.86%, outperforming existing models such as SVM (89.23% accuracy), QSVM (90.13%), and QVKSVM (97.34%). These results demonstrate that the proposed hybrid QML–DL framework effectively handles high-dimensional clinical data, reduces computational overhead, and provides a strong foundation for next-generation healthcare analytics.