Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks. (arXiv:2210.09974v1 [quant-ph])

Despite the great promise of quantum machine learning models, there are
several challenges one must overcome before unlocking their full potential. For
instance, models based on quantum neural networks (QNNs) can suffer from
excessive local minima and barren plateaus in their training landscapes.
Recently, the nascent field of geometric quantum machine learning (GQML) has
emerged as a potential solution to some of those issues. The key insight of
GQML is that one should design architectures, such as equivariant QNNs,
encoding …

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