Quantum Sinusoidal Kolmogorov Arnold Networks for High Energy Physics

The High Luminosity LHC program requires novel computational approaches to process massive datasets and identify rare signals. This project implements Quantum Kolmogorov Arnold Networks using the PennyLane framework to classify high energy physics events, such as quark gluon jet discrimination. By substituting classical linear weights with parameterized quantum circuits, we aim to benchmark the expressivity, parameter efficiency, and performance of quantum KANs against classical Multi Layer Perceptrons using CMS open datasets.