Expressivity of IQP Feature-Map Quantum Kernels via Reproducing Kernel Hilbert Spaces
A new arXiv preprint examines the expressivity of quantum kernels built from instantaneous quantum polynomial (IQP) feature maps by analysing their reproducing kernel Hilbert spaces. It frames the learning behaviour of these quantum machine learning models in terms of the function classes they represent. The abstract focuses on this formal structure rather than reporting empirical benchmarks.
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What this could mean
- 0–2 yearsPlausible
This could give practitioners a theoretically grounded way to select IQP feature maps for near-term quantum kernel experiments, narrowing which problems are worth running on quantum hardware.
If the RKHS characterisation yields explicit expressivity bounds or function-class descriptions, researchers can compare IQP kernels against classical kernels before committing to hardware runs, fitting into existing small-scale QML benchmarking over the next two years.
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