Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures

Michael Kölle, Jonas Maurer, Philipp Altmann, Leo Sünkel, Jonas Stein, Claudia Linnhoff-Popien

2024

Abstract

Quantum computing offers the potential for superior computational capabilities, particularly for data-intensive tasks. However, the current state of quantum hardware puts heavy restrictions on input size. To address this, hybrid transfer learning solutions have been developed, merging pre-trained classical models, capable of handling extensive inputs, with variational quantum circuits. Yet, it remains unclear how much each component – classical and quantum – contributes to the model’s results. We propose a novel hybrid architecture: instead of utilizing a pre-trained network for compression, we employ an autoencoder to derive a compressed version of the input data. This compressed data is then channeled through the encoder part of the autoencoder to the quantum component. We assess our model’s classification capabilities against two state-of-the-art hybrid transfer learning architectures, two purely classical architectures and one quantum architecture. Their accuracy is compared across four datasets: Banknote Authentication, Breast Cancer Wisconsin, MNIST digits, and AudioMNIST. Our research suggests that classical components significantly influence classification in hybrid transfer learning, a contribution often mistakenly ascribed to the quantum element. The performance of our model aligns with that of a variational quantum circuit using amplitude embedding, positioning it as a feasible alternative.

Paper Citation

in Harvard Style

Kölle M., Maurer J., Altmann P., Sünkel L., Stein J. and Linnhoff-Popien C. (2024). Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 649-656. DOI: 10.5220/0012381600003636

in Bibtex Style

@conference{icaart24,
author={Michael Kölle and Jonas Maurer and Philipp Altmann and Leo Sünkel and Jonas Stein and Claudia Linnhoff-Popien},
title={Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={649-656},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012381600003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Disentangling Quantum and Classical Contributions in Hybrid Quantum Machine Learning Architectures
SN - 978-989-758-680-4
AU - Kölle M.
AU - Maurer J.
AU - Altmann P.
AU - Sünkel L.
AU - Stein J.
AU - Linnhoff-Popien C.
PY - 2024
SP - 649
EP - 656
DO - 10.5220/0012381600003636
PB - SciTePress