Spectral Geometry and Bosonic-Bloch Probes: Explorations in Quantum Learning
This paper studies how spectral geometry emerges in quantum learning models and how it can be diagnosed with physically grounded probes. In graph-regularized quantum networks, training reorganizes the output similarity graph, increases the effective spectral dimension Delta S = +0.23, and reshapes the Laplacian spectrum. Edge-resolved two-boson interference directly probes this restructuring: the bosonic enhancement Delta P_uv correlates with the Fiedler edge split |Delta v_2| (r = -0.50), linking learned spectral partitions to interference signatures. A phase diagram shows a nonmonotonic dependence of performance on coupling strength gamma and noise delta, with graph regularization impro...
| Authors | Santanu Ganguly, Xing Liang, Dimitrios Makris |
|---|---|
| Primary category | quant-ph |
| Published | 30 June 2026 |
| Last updated at source | 8 September 2026 |
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