A discretization-plus-coarse-graining scheme turns continuous-space interacting particles into a tensor-network-representable lattice model, enabling partition-function calculations for the 2D hard-disk problem.
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A reverse third-quantization mapping sends Fermi-Hubbard ground states to dissipative Majorana steady states where individual Gaussian trajectories are polynomial but the non-Markovian sign problem restores exponential total cost.
Numerical tensor-network study identifies Néel, Ising, collinear, and incommensurate spiral phases plus their transitions in the J1-J2 XY antiferromagnet on the honeycomb lattice.
citing papers explorer
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Statistical mechanics in continuous space with tensor network methods
A discretization-plus-coarse-graining scheme turns continuous-space interacting particles into a tensor-network-representable lattice model, enabling partition-function calculations for the 2D hard-disk problem.
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Calculating strongly correlated ground states from the non-Markovian dissipative dynamics of Gaussian fermions
A reverse third-quantization mapping sends Fermi-Hubbard ground states to dissipative Majorana steady states where individual Gaussian trajectories are polynomial but the non-Markovian sign problem restores exponential total cost.
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Magnetic phases in the $J_{1}$-$J_{2}$ antiferromagnetic XY model on the honeycomb lattice
Numerical tensor-network study identifies Néel, Ising, collinear, and incommensurate spiral phases plus their transitions in the J1-J2 XY antiferromagnet on the honeycomb lattice.