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

• URL: http://arxiv.org/abs/2504.06892v1
• Date: Wed, 09 Apr 2025 13:53:27 GMT
• Title: Applications of Hybrid Machine Learning Methods to Large Datasets: A Case Study
• Authors: G. Maragkopoulos, N. Stefanakos, A. Mandilara, D. Syvridis,
• Abstract summary: We show 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.<n>Our results highlight the importance of tailored data pre-processing for the circuit and show the potential of qudit-based VQCs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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