Proves mean-field limit and propagation of chaos for gradient-flow trained mixtures of experts with explicit rate depending only on expert count, applied to quantum neural networks.
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An efficient classical algorithm reduces the NTK average for Clifford-Pauli quantum neural networks to four discrete Clifford gates, enabling Gaussian-process simulation of wide trained networks and ruling out quantum advantage for this class.
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Mean-field limit from general mixtures of experts to quantum neural networks
Proves mean-field limit and propagation of chaos for gradient-flow trained mixtures of experts with explicit rate depending only on expert count, applied to quantum neural networks.
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Efficient classical computation of the neural tangent kernel of quantum neural networks
An efficient classical algorithm reduces the NTK average for Clifford-Pauli quantum neural networks to four discrete Clifford gates, enabling Gaussian-process simulation of wide trained networks and ruling out quantum advantage for this class.