"Quantum Neural Networks: Size vs Efficiency"

Increasing the number of parameters in a quantum neural network leads to a computational ‘phase transition’, beyond which training the network becomes significantly easier, according to an algebraic theory developed for this overparametrization phenomenon. The theory predicts its onset above a certain parameter threshold.