Applications of Hybrid Machine Learning Methods to Large Datasets: A Case Study

2025/04/09 by G. Maragkopoulos, Maragkopoulos, G., Nikolaos Stefanakos +5 · 1 citation
Computer Science · Physics and Astronomy · Materials Science · #Quantum Computing Algorithms and Architecture #Quantum many-body systems #Machine Learning in Materials Science

paper · pdf · doi:10.48550/arxiv.2504.06892

Abstract

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.

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