Python library to compute properties of quantum tight binding models, including topological, electronic and magnetic properties and including the effect of many-body interactions.
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Updated
Aug 30, 2026 - Jupyter Notebook
Python library to compute properties of quantum tight binding models, including topological, electronic and magnetic properties and including the effect of many-body interactions.
Accompanying repo for arXiv 2512.18397
A physics-informed machine learning approach for analytic continuation of Green’s functions. Uses a Variational Autoencoder (VAE) to reconstruct spectral functions by explicitly learning poles and residues from imaginary-time Quantum Monte Carlo data. Robust, interpretable, and designed for noisy inputs.
Dynamical spectral functions from bitstring-sampled quantum subspaces — paper, code & full reproducibility (entanglement, not one-body magic, tracks the sampling cost)
A testing toolkit for analytic continuation methods and codes
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