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Authors: Horst Stühler 1 ; Daniel Pranjić 2 and Christian Tutschku 2

Affiliations: 1 Zeppelin GmbH, Graf-Zeppelin-Platz 1, 85766 Garching, Germany ; 2 Fraunhofer IAO, Nobelstraße 12, 70569 Stuttgart, Germany

Keyword(s): Price Forecasting, Machine Learning, ML, Quantum Machine Learning, QML, SVR, QSVR.

Abstract: Support vector machines are powerful and frequently used machine learning methods for classification and regression tasks, which rely on the construction of kernel matrices. While crucial for the performance of this machine learning approach, choosing the most suitable kernel is highly problem-dependent. The emergence of quantum computers and quantum machine learning techniques provides new possibilities for generating powerful quantum kernels. Within this work, we solve a real-world price forecasting problem using fidelity and projected quantum kernels, which are promising candidates for the utility of near-term quantum computing. In our analysis, we examine and validate the most auspicious quantum kernels from literature and compare their performance with an optimized classical kernel. Unlike previous work on quantum support vector machines, our dataset includes categorical features that need to be encoded as numerical features, which we realize by using the one-hot-encoding scheme . One-hot-encoding, however, increases the dimensionality of the dataset significantly, which collides with the current limitations of noisy intermediate scale quantum computers. To overcome these limitations, we use autoencoders to learn a low-dimensional representation of the feature space that still maintains the most important information of the original data. To examine the impact of autoencoding, we compare the results of the encoded date with the results of the original, unencoded dataset. We could demonstrate that quantum kernels are comparable to or even better than the classical support vector machine kernels regarding the mean absolute percentage error scores for both encoded and unencoded datasets. (More)

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Paper citation in several formats:
Stühler, H.; Pranjić, D. and Tutschku, C. (2024). Evaluating Quantum Support Vector Regression Methods for Price Forecasting Applications. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 376-384. DOI: 10.5220/0012351400003636

@conference{icaart24,
author={Horst Stühler. and Daniel Pranjić. and Christian Tutschku.},
title={Evaluating Quantum Support Vector Regression Methods for Price Forecasting Applications},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={376-384},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012351400003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Evaluating Quantum Support Vector Regression Methods for Price Forecasting Applications
SN - 978-989-758-680-4
IS - 2184-433X
AU - Stühler, H.
AU - Pranjić, D.
AU - Tutschku, C.
PY - 2024
SP - 376
EP - 384
DO - 10.5220/0012351400003636
PB - SciTePress