A coherence law based on the readout-visible aligned coherence rate (a Rayleigh quotient of the noise generator) predicts gradient survival in noisy U(1)-equivariant QNNs, with simulations confirming R²=0.979 and a special channel test showing no loss where predicted.
Symmetry-organised complexity in quantum neural networks,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
Presents an observer-explicit diagnostic framework that separates inhomogeneity, expansion variance, shear, and Weyl curvature contributions in cosmological solutions of Einstein's equations, tested on six benchmark families with public code.
citing papers explorer
-
A Coherence Law for Trainability in Noisy Equivariant Quantum Neural Networks
A coherence law based on the readout-visible aligned coherence rate (a Rayleigh quotient of the noise generator) predicts gradient survival in noisy U(1)-equivariant QNNs, with simulations confirming R²=0.979 and a special channel test showing no loss where predicted.