Spectral invariance and maximality properties of the frequency spectrum of quantum neural networks

arXiv:2402.14515v1 Announce Type: cross
Abstract: Quantum Neural Networks (QNNs) are a popular approach in Quantum Machine Learning due to their close connection to Variational Quantum Circuits, making them a promising candidate for practical applications on Noisy Intermediate-Scale Quantum (NISQ) devices. A QNN can be expressed as a finite Fourier series, where the set of frequencies is called the frequency spectrum. We analyse this frequency spectrum and prove, for a large class of models, various maximality results. Furthermore, we prove that …

abstract applications arxiv cs.lg devices intermediate machine machine learning making networks neural networks nisq popular practical quant-ph quantum quantum neural networks scale spectrum stat.ml them type