Why bigger quantum neural networks do better

Increasing the number of parameters in a quantum neural network leads to a computational 'phase transition', beyond which training the network becomes significantly easier. An algebraic theory has been developed for this overparametrization phenomenon and predicts its onset above a certain parameter threshold.

Automated summary

Increasing the number of parameters in a quantum neural network leads to a computational 'phase transition' where training the network becomes significantly easier. An algebraic theory has been developed to explain this overparametrization phenomenon and predicts its occurrence above a certain parameter threshold.