Spectral invariance and maximality properties of the frequency spectrum of quantum neural networks
<|reference_start|>Spectral invariance and maximality properties of the frequency spectrum of quantum neural networks: 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 under some mild conditions there exists a bijection between classes of models with the same area $A = RL$ that preserves the frequency spectrum, where $R$ denotes the number of qubits and $L$ the number of layers, which we consequently call spectral invariance under area-preserving transformations. With this we explain the symmetry in $R$ and $L$ in the results often observed in the literature and show that the maximal frequency spectrum depends only on the area $A = RL$ and not on the individual values of $R$ and $L$. Moreover, we extend existing results and specify the maximum possible frequency spectrum of a QNN with arbitrarily many layers as a function of the spectrum of its generators. If the generators of the QNN can be further decomposed into 2-dimensional sub-generators, then this specification follows from elementary number-theoretical considerations. In the case of arbitrary dimensional generators, we extend existing results based on the so-called Golomb ruler and introduce a second novel approach based on a variation of the turnpike problem, which we call the relaxed turnpike problem.<|reference_end|>
@article{holzer2024spectral,
    title={Spectral invariance and maximality properties of the frequency spectrum
  of quantum neural networks},
    author={Patrick Holzer, Ivica Turkalj},
    journal={arXiv preprint arXiv:2402.14515},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14515},
    primaryClass={quant-ph cs.LG stat.ML}
}