The Impact of Feature Embedding Placement in the Ansatz of a Quantum Kernel in QSVMs
<|reference_start|>The Impact of Feature Embedding Placement in the Ansatz of a Quantum Kernel in QSVMs: Designing a useful feature map for a quantum kernel is a critical task when attempting to achieve an advantage over classical machine learning models. The choice of circuit architecture, i.e. how feature-dependent gates should be interwoven with other gates is a relatively unexplored problem and becomes very important when using a model of quantum kernels called Quantum Embedding Kernels (QEK). We study and categorize various architectural patterns in QEKs and show that existing architectural styles do not behave as the literature supposes. We also produce a novel alternative architecture based on the old ones and show that it performs equally well while containing fewer gates than its older counterparts.<|reference_end|>
@article{salmenperä2024the,
    title={The Impact of Feature Embedding Placement in the Ansatz of a Quantum
  Kernel in QSVMs},
    author={Ilmo Salmenper"a, Ilmars Kuhtarskis, Arianne Meijer van de Griend,
  Jukka K. Nurminen},
    journal={arXiv preprint arXiv:2409.13147},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13147},
    primaryClass={quant-ph cs.AI}
}