We combine classical and quantum Machine Learning (ML) techniques to effectively analyze long time-series data acquired during experiments. Specifically, we demonstrate that replacing a deep classical neural network with a thoughtfully designed Variational Quantum Circuit (VQC) in an ML pipeline for multiclass classification of time-series data yields the same classification performance, while significantly reducing the number of trainable parameters. To achieve this, we use a VQC based on a single qudit, and encode the classical data into the VQC via a trainable hybrid autoencoder which has been recently proposed as embedding technique. Our results highlight the importance of tailored data pre-processing for the circuit and show the potential of qudit-based VQCs.
@article{arxiv.2504.06892,
title = {Applications of Hybrid Machine Learning Methods to Large Datasets: A Case Study},
author = {G. Maragkopoulos and N. Stefanakos and A. Mandilara and D. Syvridis},
journal= {arXiv preprint arXiv:2504.06892},
year = {2025}
}