Number-conserving fermionic shadow tomography estimates all k-body correlations in η-particle N-mode states using O_k(η^k/ε²) samples independent of N, with a matching Ω_k(η^k/ε²) lower bound for single-copy adaptive protocols.
Structure of Fermion Density Matrices
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
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.
A unified framework for functional theories of quantum systems is introduced via scopes of observables and fixed Hamiltonian parts, enabling general proofs of universal functionals, convexity, differentiability, representability, and Hohenberg-Kohn-type uniqueness across variants.
Neural ODEs reproduce 2RDM dynamics from data only when three-particle cumulant correlations are strong, mapping the validity regime of cumulant expansions.
citing papers explorer
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Provably Efficient Learning of Fermionic Correlations under Particle-Number Symmetry
Number-conserving fermionic shadow tomography estimates all k-body correlations in η-particle N-mode states using O_k(η^k/ε²) samples independent of N, with a matching Ω_k(η^k/ε²) lower bound for single-copy adaptive protocols.
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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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Unified Framework for Functional Theories of Quantum Systems
A unified framework for functional theories of quantum systems is introduced via scopes of observables and fixed Hamiltonian parts, enabling general proofs of universal functionals, convexity, differentiability, representability, and Hohenberg-Kohn-type uniqueness across variants.
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Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
Neural ODEs reproduce 2RDM dynamics from data only when three-particle cumulant correlations are strong, mapping the validity regime of cumulant expansions.