Simulations and analysis showing that gradient loss in noisy U(1)-equivariant quantum neural networks is governed by readout-visible sector coherence. Density-matrix simulations, regression analysis, and reproducibility code for a study of noise-induced gradient degradation in equivariant brickwork QNNs.
quantum-computing quantum-machine-learning open-quantum-systems quantum-neural-networks quantum-noise variational-quantum-algorithms trainability barren-plateaus quantum-coherence geometric-quantum-machine-learning equivariant-circuits
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Updated
Jul 2, 2026 - Jupyter Notebook