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.
Robustness of asymmetry and coherence of quantum states,
2 Pith papers cite this work. Polarity classification is still indexing.
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Presents optimization framework and closed-form solutions for convex approximation of quantum channels under α-affinity metric for SU(2)-covariant, Pauli, and amplitude-damping cases.
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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.
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Optimal convex approximation of quantum channels based on $\alpha$-affinity
Presents optimization framework and closed-form solutions for convex approximation of quantum channels under α-affinity metric for SU(2)-covariant, Pauli, and amplitude-damping cases.