saranyanalluri/comparative-cml-qml-benchmarking

This repository is a set of runnable experiments comparing classical and quantum or quantum-enhanced machine-learning models across synthetic and real datasets. The principal workflow is to construct or load a dataset, optionally preprocess it, evaluate models using cross-validation, calculate accuracy and timing metrics, and save or summarize results. Experiments cover Iris, crop recommendation, RadioML, clustering/moons, Ising, ad hoc, bit parity, and bars-and-stripes; visual.py separately plots Iris and ad hoc data. Source excerpts are partial, so links from individual scripts to the collected result files are not asserted where output wiring is uncertain.