At fixed encoding budget, serial QNN architectures suffer unbounded structural gradient starvation via rank(J) ≤ 2L+1 while parallel ones keep full Jacobian rank and better parameter efficiency when adding feature-map layers.
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EstemPMM is an R package that implements the Polynomial Maximization Method using third and fourth cumulants to improve parameter estimation over OLS for asymmetric or leptokurtic errors in regression and ARMA models.
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Architecture Shape Governs QNN Trainability: Jacobian Null Space Growth and Parameter Efficiency
At fixed encoding budget, serial QNN architectures suffer unbounded structural gradient starvation via rank(J) ≤ 2L+1 while parallel ones keep full Jacobian rank and better parameter efficiency when adding feature-map layers.
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EstemPMM: Polynomial Maximization Method for Non-Gaussian Regression and Time Series in R
EstemPMM is an R package that implements the Polynomial Maximization Method using third and fourth cumulants to improve parameter estimation over OLS for asymmetric or leptokurtic errors in regression and ARMA models.