Maheshwari, DanyalSierra-Sosa, DanielGarcía-Zapirain, Begoña2024-11-142024-11-142022Maheshwari, D., Sierra-Sosa, D., & Garcia-Zapirain, B. (2022). Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset. IEEE Access, 10, 3705-3715. https://doi.org/10.1109/ACCESS.2021.31393232169-353610.1109/ACCESS.2021.3139323http://hdl.handle.net/20.500.14454/1860Nowadays, quantum-enhanced methods have been widely studied to solve machine learning related problems. This article presents the application of a Variational Quantum Classifier (VQC) for binary classification. We utilized three datasets: a synthetic dataset with randomly generated values between 0 and 1, the publicly available University of California Intelligence Machine learning (UCI) sonar dataset consisting of mining data, and a proprietary diabetes dataset related to diabetes with acute diseases and diabetes without acute disease. To deal with the limitation of noisy intermediate-scale quantum systems (NISQ), we used a pre-processing method to enhance the prediction rate when applying the VQC method. The process includes feature selection and state preparation. Quantum state preparation is critical for obtaining a functioning pipeline in a quantum machine learning (QML) model. Amplitude encoding is a state preparation approach that enhances the performance of data encoding and the learning of quantum models. As a result, our proposed methods achieved accuracies of 75%, 71.4%, and 68.73% by using VQC model and in contrast, the amplitude encoding-based VQC achieved 98.40%, 67.3%, and 74.50% accuracies on the synthetic, sonar, and diabetes dataset, respectively.engAmplitude encodingQuantum machine learningState preparationVariational quantum classifier and T2DM diabetesjournal article2024-11-14