Equilibrium quantum many-body methods are encoders from admissible states to represented variables, with exact decoders existing precisely when tasks are constant on encoder fibers.
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Derives static effective Hamiltonians via cRPA and mRPA downfolding with double-counting corrections and compares performance on benzene ground state and bond dissociation curves.
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.
citing papers explorer
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Full-State and Reduced-Moment Encodings: A Representation-Level View of Equilibrium Quantum Many-Body Theory
Equilibrium quantum many-body methods are encoders from admissible states to represented variables, with exact decoders existing precisely when tasks are constant on encoder fibers.
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Static Effective Hamiltonians for Molecular Systems through RPA-based downfolding
Derives static effective Hamiltonians via cRPA and mRPA downfolding with double-counting corrections and compares performance on benzene ground state and bond dissociation curves.
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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.