RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.
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QAssemble is a new pure-Python package for quantum many-body calculations that achieves up to 60x speedup via vectorization and discrete Lehmann representation while validating on graphene.
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Discovering a well-conditioned analytic continuation problem via dictionary learning
RSOM applies dictionary learning to discover a sparse dictionary that conditions the analytic continuation inverse problem, yielding competitive results on synthetic tests and finite-temperature electron gas QMC data.
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QAssemble: A Pure Python Package for Quantum Many-Body Theory
QAssemble is a new pure-Python package for quantum many-body calculations that achieves up to 60x speedup via vectorization and discrete Lehmann representation while validating on graphene.