Introduces an efficient SSE QMC algorithm with global updates and parallel tempering for mixed-dimensional models and applies it to map angle-dependent correlated insulators and Wigner-Mott states in M-point twisted AA-stacked SnSe2.
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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.
svPITE is a Python package for ground-state preparation via probabilistic imaginary-time evolution, supporting state-vector and shot-based modes with exact-diagonalization benchmarking and interoperability for dynamical observables.
citing papers explorer
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Mixed-dimensional quantum Monte Carlo studies of M-point moir\'e materials
Introduces an efficient SSE QMC algorithm with global updates and parallel tempering for mixed-dimensional models and applies it to map angle-dependent correlated insulators and Wigner-Mott states in M-point twisted AA-stacked SnSe2.
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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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svPITE: A Python package for the state-vector-based probabilistic imaginary-time evolution algorithm
svPITE is a Python package for ground-state preparation via probabilistic imaginary-time evolution, supporting state-vector and shot-based modes with exact-diagonalization benchmarking and interoperability for dynamical observables.