Neural quantum states on K5 yield two families of approximate physical states for the Thiemann-ordered Hamiltonian constraint in Abelianized Euclidean LQG: one flat with non-zero volume (non-normalizable) and one normalizable with zero volume, close to Ashtekar-Lewandowski and Dittrich-Geiller vacua
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UNVERDICTED 11representative citing papers
Relativistic continuous matrix product states yield competitive variational approximations to ground state energies and observables in the phi^4, Sine-Gordon, and Sinh-Gordon models, including strongly coupled regimes.
NQS performance for TFIM ground states depends on basis choice through ground-state degeneracies and amplitude-phase uniformity, which control the convergence of multi-spin cumulant expansions.
A new framework certifies global quantum properties including multipartite entanglement, circuit complexity, and quantum magic on small subsystems with constant sample complexity via local Pauli measurements.
EBMs trained with non-persistent short runs reproduce empirical data statistics via a precise dynamical process, not the equilibrium measure.
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
Machine learning reconstruction accuracy is substantially higher for spectral-edge eigenstates than for mid-spectrum eigenstates, providing a new quantitative measure of information content in many-body quantum states.
Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
Variational autoencoders combined with symbolic regression extract physically meaningful representations and order parameters from raw quantum measurement data, revealing new phenomena such as corner-ordering in Rydberg arrays.
A discretized higher-rank gauge theory on a square lattice produces a classical Ising fracton spin liquid with preserved tensor Gauss law, but quantum perturbations induce severe Hilbert space fragmentation that blocks fractonic quantum dynamics.
A shallow restricted Boltzmann machine variational Monte Carlo ansatz reproduces the main features of the adiabatic phase diagram and selected symmetry-broken insulating states for the one-dimensional Z2 Bose-Hubbard chain at half filling.
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Finding and characterising physical states of Euclidean Abelianized loop quantum gravity using neural quantum states
Neural quantum states on K5 yield two families of approximate physical states for the Thiemann-ordered Hamiltonian constraint in Abelianized Euclidean LQG: one flat with non-zero volume (non-normalizable) and one normalizable with zero volume, close to Ashtekar-Lewandowski and Dittrich-Geiller vacua
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Some progress on the use of the variational method in quantum field theory
Relativistic continuous matrix product states yield competitive variational approximations to ground state energies and observables in the phi^4, Sine-Gordon, and Sinh-Gordon models, including strongly coupled regimes.
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Basis dependence of Neural Quantum States for the Transverse Field Ising Model
NQS performance for TFIM ground states depends on basis choice through ground-state degeneracies and amplitude-phase uniformity, which control the convergence of multi-spin cumulant expansions.
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Certifying localizable quantum properties with constant sample complexity
A new framework certifies global quantum properties including multipartite entanglement, circuit complexity, and quantum magic on small subsystems with constant sample complexity via local Pauli measurements.
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Explaining the effects of non-convergent sampling in the training of Energy-Based Models
EBMs trained with non-persistent short runs reproduce empirical data statistics via a precise dynamical process, not the equilibrium measure.
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Absorbing Many-Body Correlations into Core-Optimized Orbitals
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
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Information in Many-body Eigenstates: A Question of Learnability
Machine learning reconstruction accuracy is substantially higher for spectral-edge eigenstates than for mid-spectrum eigenstates, providing a new quantitative measure of information content in many-body quantum states.
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Neural-Network-Based Variational Method in Nuclear Density Functional Theory: Application to the Extended Thomas-Fermi Model
Neural networks represent densities in a variational extended Thomas-Fermi model, yielding binding energies within 0.5% of prior ETF results and reproducing nuclear pasta phases.
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Discovering quantum phenomena with Interpretable Machine Learning
Variational autoencoders combined with symbolic regression extract physically meaningful representations and order parameters from raw quantum measurement data, revealing new phenomena such as corner-ordering in Rydberg arrays.
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Classical fracton spin liquid and Hilbert space fragmentation in a 2D spin-$1/2$ model
A discretized higher-rank gauge theory on a square lattice produces a classical Ising fracton spin liquid with preserved tensor Gauss law, but quantum perturbations induce severe Hilbert space fragmentation that blocks fractonic quantum dynamics.
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Benchmarking a restricted Boltzmann machine on the $\mathbb{Z}_2$ Bose-Hubbard chain in the adiabatic hard-core regime
A shallow restricted Boltzmann machine variational Monte Carlo ansatz reproduces the main features of the adiabatic phase diagram and selected symmetry-broken insulating states for the one-dimensional Z2 Bose-Hubbard chain at half filling.