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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Canonical mapping of quantum-dot-superconductor clusters enables neural quantum-state calculations that reveal trivial singlet, Heisenberg-like, and critical regimes with 1D gaplessness and 2D triplet states.
QMP-Bench supplies a realistic test set for AI on quantum many-body problems while PhysVEC uses integrated verifiers to turn unreliable LLM generations into code that passes both syntax and physics checks, outperforming baselines.
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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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Correlated States in Quantum Dot Clusters Coupled to a Common Superconductor
Canonical mapping of quantum-dot-superconductor clusters enables neural quantum-state calculations that reveal trivial singlet, Heisenberg-like, and critical regimes with 1D gaplessness and 2D triplet states.
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Towards Verifiable and Self-Correcting AI Physicists for Quantum Many-Body Simulations
QMP-Bench supplies a realistic test set for AI on quantum many-body problems while PhysVEC uses integrated verifiers to turn unreliable LLM generations into code that passes both syntax and physics checks, outperforming baselines.