Web: http://arxiv.org/abs/2209.11144

Sept. 23, 2022, 1:11 a.m. | Massimiliano Incudini, Francesco Martini, Alessandra Di Pierro

cs.LG updates on arXiv.org arxiv.org

The representation of data is of paramount importance for machine learning
methods. Kernel methods are used to enrich the feature representation, allowing
better generalization. Quantum kernels implement efficiently complex
transformation encoding classical data in the Hilbert space of a quantum
system, resulting in even exponential speedup. However, we need prior knowledge
of the data to choose an appropriate parametric quantum circuit that can be
used as quantum embedding. We propose an algorithm that automatically selects
the best quantum embedding through …

arxiv quantum

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