SEQUENT: Towards Traceable Quantum Machine Learning Using Sequential Quantum Enhanced Training

Philipp Altmann, Leo Sünkel, Jonas Stein, Tobias Müller, Christoph Roch, Claudia Linnhoff-Popien

2023

Abstract

Applying new computing paradigms like quantum computing to the field of machine learning has recently gained attention. However, as high-dimensional real-world applications are not yet feasible to be solved using purely quantum hardware, hybrid methods using both classical and quantum machine learning paradigms have been proposed. For instance, transfer learning methods have been shown to be successfully applicable to hybrid image classification tasks. Nevertheless, beneficial circuit architectures still need to be explored. Therefore, tracing the impact of the chosen circuit architecture and parameterization is crucial for the development of beneficially applicable hybrid methods. However, current methods include processes where both parts are trained concurrently, therefore not allowing for a strict separability of classical and quantum impact. Thus, those architectures might produce models that yield a superior prediction accuracy whilst employing the least possible quantum impact. To tackle this issue, we propose Sequential Quantum Enhanced Training (SEQUENT) an improved architecture and training process for the traceable application of quantum computing methods to hybrid machine learning. Furthermore, we provide formal evidence for the disadvantage of current methods and preliminary experimental results as a proof-of-concept for the applicability of SEQUENT.

Paper Citation

in Harvard Style

Altmann P., Sünkel L., Stein J., Müller T., Roch C. and Linnhoff-Popien C. (2023). SEQUENT: Towards Traceable Quantum Machine Learning Using Sequential Quantum Enhanced Training. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 744-751. DOI: 10.5220/0011772400003393

in Bibtex Style

@conference{icaart23,
author={Philipp Altmann and Leo Sünkel and Jonas Stein and Tobias Müller and Christoph Roch and Claudia Linnhoff-Popien},
title={SEQUENT: Towards Traceable Quantum Machine Learning Using Sequential Quantum Enhanced Training},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={744-751},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011772400003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - SEQUENT: Towards Traceable Quantum Machine Learning Using Sequential Quantum Enhanced Training
SN - 978-989-758-623-1
AU - Altmann P.
AU - Sünkel L.
AU - Stein J.
AU - Müller T.
AU - Roch C.
AU - Linnhoff-Popien C.
PY - 2023
SP - 744
EP - 751
DO - 10.5220/0011772400003393