Introduces a gauge-geometry framework that computes curvature and holonomy of Hodge zero-mode transport to detect structural changes in parameter-dependent topological data.
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Training in graph-regularized quantum networks increases spectral dimension by 0.23 and enables anomaly detection via Bloch drift (ROC-AUC ≥0.9) while bosonic enhancement correlates with Fiedler splits (r=-0.50).
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Gauge Geometry of Hodge Zero-Mode Transport in Parameter-Dependent Topological Data Analysis
Introduces a gauge-geometry framework that computes curvature and holonomy of Hodge zero-mode transport to detect structural changes in parameter-dependent topological data.