Group-Invariant Quantum Machine Learning. (arXiv:2205.02261v2 [quant-ph] UPDATED)

Quantum Machine Learning (QML) models are aimed at learning from data encoded
in quantum states. Recently, it has been shown that models with little to no
inductive biases (i.e., with no assumptions about the problem embedded in the
model) are likely to have trainability and generalization issues, especially
for large problem sizes. As such, it is fundamental to develop schemes that
encode as much information as available about the problem at hand. In this work
we present a simple, yet …

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