Benchmarking Quantum Surrogate Models on Scarce and Noisy Data

Jonas Stein; Michael Poppel; Philip Adamczyk; Ramona Fabry; Zixin Wu; Michael Kölle; Jonas Nüßlein; Daniëlle Schuman; Philipp Altmann; Thomas Ehmer; Vijay Narasimhan; Claudia Linnhoff-Popien

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Paper

Benchmarking Quantum Surrogate Models on Scarce and Noisy Data

Topics: Quantum Artificial Intelligence

In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 352-359, 2024 , Rome, Italy

Authors: Jonas Stein ¹ ; Michael Poppel ¹ ; Philip Adamczyk ¹ ; Ramona Fabry ¹ ; Zixin Wu ¹ ; Michael Kölle ¹ ; Jonas Nüßlein ¹ ; Daniëlle Schuman ¹ ; Philipp Altmann ¹ ; Thomas Ehmer ² ; Vijay Narasimhan ³ and Claudia Linnhoff-Popien ¹

Affiliations: ¹ LMU Munich, Germany ; ² Merck KGaA, Darmstadt, Germany ; ³ EMD Electronics, San Jose, California, U.S.A.

Keyword(s): Quantum Computing, Surrogate Model, NISQ, QNN.

Abstract: Surrogate models are ubiquitously used in industry and academia to efficiently approximate black box functions. As state-of-the-art methods from classical machine learning frequently struggle to solve this problem accurately for the often scarce and noisy data sets in practical applications, investigating novel approaches is of great interest. Motivated by recent theoretical results indicating that quantum neural networks (QNNs) have the potential to outperform their classical analogs in the presence of scarce and noisy data, we benchmark their qualitative performance for this scenario empirically. Our contribution displays the first application-centered approach of using QNNs as surrogate models on higher dimensional, real world data. When compared to a classical artificial neural network with a similar number of parameters, our QNN demonstrates significantly better results for noisy and scarce data, and thus motivates future work to explore this potential quantum advantage. Finally , we demonstrate the performance of current NISQ hardware experimentally and estimate the gate fidelities necessary to replicate our simulation results. (More)

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Paper citation in several formats:

Stein, J.; Poppel, M.; Adamczyk, P.; Fabry, R.; Wu, Z.; Kölle, M.; Nüßlein, J.; Schuman, D.; Altmann, P.; Ehmer, T.; Narasimhan, V. and Linnhoff-Popien, C. (2024). Benchmarking Quantum Surrogate Models on Scarce and Noisy Data. 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 352-359. DOI: 10.5220/0012348900003636

@conference{icaart24,
author={Jonas Stein. and Michael Poppel. and Philip Adamczyk. and Ramona Fabry. and Zixin Wu. and Michael Kölle. and Jonas Nüßlein. and Daniëlle Schuman. and Philipp Altmann. and Thomas Ehmer. and Vijay Narasimhan. and Claudia Linnhoff{-}Popien.},
title={Benchmarking Quantum Surrogate Models on Scarce and Noisy Data},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={352-359},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012348900003636},
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 - Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
SN - 978-989-758-680-4
IS - 2184-433X
AU - Stein, J.
AU - Poppel, M.
AU - Adamczyk, P.
AU - Fabry, R.
AU - Wu, Z.
AU - Kölle, M.
AU - Nüßlein, J.
AU - Schuman, D.
AU - Altmann, P.
AU - Ehmer, T.
AU - Narasimhan, V.
AU - Linnhoff-Popien, C.
PY - 2024
SP - 352
EP - 359
DO - 10.5220/0012348900003636
PB - SciTePress