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.
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References
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This is a summary of: Larocca, M. et al. Theory of overparametrization in quantum neural networks. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00467-6 (2023).
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Why bigger quantum neural networks do better. Nat Comput Sci 3, 484–485 (2023). https://doi.org/10.1038/s43588-023-00468-5
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DOI: https://doi.org/10.1038/s43588-023-00468-5
