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geometric-quantum-machine-learning

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ugail / Readout-Visible-Coherence-QNN

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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
  • Updated Jul 2, 2026
  • Jupyter Notebook

ugail / PsiAudit

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A reproducible toolkit for auditing symmetry-organised complexity in equivariant quantum neural network ansatz, reporting sector occupation, cross-sector coherence, sectoral fluctuation, and generator-sum compliance against U(1), SU(2), and permutation symmetry before training.

quantum-computing symmetry symmetry-breaking quantum-machine-learning variational-quantum-circuits barren-plateaus expressibility near-term-quantum-computing equivariant-quantum-neural-networks parameterised-quantum-circuits ansatz-diagnostics meyer-wallach-entanglement geometric-quantum-machine-learning
  • Updated May 31, 2026
  • Python

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