Quantum-Efficient Kernel Target Alignment

Rodrigo Coelho, Georg Kruse, Georg Kruse, Andreas Rosskopf

2025

Abstract

In recent years, quantum computers have emerged as promising candidates for implementing kernels. Quantum Embedding Kernels embed data points into quantum states and calculate their inner product in a high-dimensional Hilbert Space by computing the overlap between the resulting quantum states. Variational Quantum Circuits (VQCs) are typically used for this end, with Kernel Target Alignment (KTA) as cost function. The optimized kernels can then be deployed in Support Vector Machines (SVMs) for classification tasks. However, both classical and quantum SVMs scale poorly with increasing dataset sizes. This issue is exacerbated in quantum kernel methods, as each inner product requires a quantum circuit execution. In this paper, we investigate KTA-trained quantum embedding kernels and employ a low-rank matrix approximation, the Nyström method, to reduce the quantum circuit executions needed to construct the Kernel Matrix. We empirically evaluate the performance of our approach across various datasets, focusing on the accuracy of the resulting SVM and the reduction in quantum circuit executions. Additionally, we examine and compare the robustness of our model under different noise types, particularly coherent and depolarizing noise.

Paper Citation

in Harvard Style

Coelho R., Kruse G. and Rosskopf A. (2025). Quantum-Efficient Kernel Target Alignment. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO; ISBN 978-989-758-737-5, SciTePress, pages 763-772. DOI: 10.5220/0013391500003890

in Bibtex Style

@conference{qaio25,
author={Rodrigo Coelho and Georg Kruse and Andreas Rosskopf},
title={Quantum-Efficient Kernel Target Alignment},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO},
year={2025},
pages={763-772},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013391500003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO
TI - Quantum-Efficient Kernel Target Alignment
SN - 978-989-758-737-5
AU - Coelho R.
AU - Kruse G.
AU - Rosskopf A.
PY - 2025
SP - 763
EP - 772
DO - 10.5220/0013391500003890
PB - SciTePress