Expressivity of IQP Feature-Map Quantum Kernels via Reproducing Kernel Hilbert Spaces
Quantum kernels are among the most promising methods in quantum machine learning, providing a natural way to embed data into high-dimensional Hilbert spaces via quantum circuits and capturing non-linear relationships in data. Their learning behaviour is governed by the function class they represent, which is formally characterized by the structure of the associated Reproducing Kernel Hilbert Space (RKHS). However, a theoretical characterization of this function class, and of how it depends on the choice of feature map and its hyperparameters, is still lacking. In this work, we focus on the Instantaneous Quantum Polynomial (IQP) feature map and derive a Fourier representation of the associ...
| Authors | Valeria Repetto, Stefano Melchionna, Romina D'Aurizio |
|---|---|
| Primary category | quant-ph |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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