Quantum Data Re-Uploading Classifiers: Empirical Performance and Comparison with State-of-the-Art Quantum Machine Learning

This white paper presents the implementation and empirical evaluation of a data re-uploading quantum classifier on a synthetic binary dataset, achieving 53% accuracy. It compares results to state-of-the-art quantum and hybrid classifiers and discusses future directions. This project is based on hands-on training with Google Data Analytics, UCSD Applied Bioinformatics, and AI-Med monthly meetings at CHOC Children’s MI3, with final refinements over a focused 30-day build sprint. The document is intended as an educational resource for quantum machine learning researchers and integrative AI practitioners.