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On the expressivity of embedding quantum kernels
Elies Gil-Fuster, Jens Eisert, Vedran Dunjko
One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are…
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Ryan Sweke, Erik Recio-Armengol, Sofiene Jerbi +4
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning…
Understanding quantum machine learning also requires rethinking generalization
Elies Gil-Fuster, Jens Eisert, Carlos Bravo-Prieto
Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we sho…