Quantum-Powered Medical Data Classification Through Variational Circuits

Abstract: In the contemporary health care system, to effectively manage the patients, it is imperative to make right forecasts of the diseases like heart disease and diabetes, among others. This article provides a Hybrid Quantum-Classical System which makes use of Quantum Variational Classifier (QVC) to predict illnesses in medicine. The suggested system takes advantage of the capabilities of quantum computing to investigate high-dimensional spaces that are generally difficult to find by classical machine learning models. Combination of quantum variational circuits and classical machine learning methods allows the effective classification of the clinical data, including age, glucose levels, and BMI. This type of system is tested by using medical datasets, and the outcomes reveal that this system is more robust, especially in the presence of noise or in case of smaller training sets. The QVC demonstrates the improvements in prediction accuracy that are promising when compared to the traditional models such as Support Vector Machines (SVM), Logistic Regression, and Neural Networks. Such a framework preconditions the next-generation quantum-powered medical diagnostics, which will be more computationally efficient and future predictions, despite limited or noisy data.

Keywords: Hybrid Quantum-Classical System, Quantum Variational Classifier, Medical Disease Prediction, Heart Disease, Diabetes, Quantum Computing, Clinical Data Classification, Noisy Data, Quantum Variational Circuits, Computational Efficiency