Let Quantum Neural Networks Choose Their Own Frequencies

arXiv:2309.03279v2 Announce Type: replace-cross
Abstract: Parameterized quantum circuits as machine learning models are typically well described by their representation as a partial Fourier series of the input features, with frequencies uniquely determined by the feature map's generator Hamiltonians. Ordinarily, these data-encoding generators are chosen in advance, fixing the space of functions that can be represented. In this work we consider a generalization of quantum models to include a set of trainable parameters in the generator, leading to a trainable frequency …

abstract advance arxiv circuits cs.lg data encoding feature features fourier functions generator generators machine machine learning machine learning models map networks neural networks quant-ph quantum quantum neural networks representation series space type