Hamiltonian and Liouvillian learning from measurement data
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Updated
Feb 22, 2026 - Python
Hamiltonian and Liouvillian learning from measurement data
Palindromic Liouvillian symmetry under dephasing: proven for any Heisenberg graph, verified N=2–8 (87,376 eigenvalues, zero exceptions). Absorption Theorem Re(λ) = −2γ⟨n_XY⟩, golden-ratio mirrors, noise that reads its own spectrum; 17 predictions confirmed on IBM hardware. A living research notebook, Thomas Wicht & Claude. R = CΨ²
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