A tensor-network method converts ultra-large tight-binding problems into compressible many-body problems on L pseudospins and evaluates observables without explicit matrix storage or diagonalization.
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Tensor network scans reveal that the stationary spin entanglement entropy ridge follows population phase boundaries at small s but lacks the two-branch structure at large s in the sub-Ohmic spin-boson model.
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Tensor Network Solvers for Ultra-large Tight-binding Hamiltonians: Algorithms and Applications
A tensor-network method converts ultra-large tight-binding problems into compressible many-body problems on L pseudospins and evaluates observables without explicit matrix storage or diagonalization.
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Entanglement structure of the dynamical phases in the sub-Ohmic spin-boson model
Tensor network scans reveal that the stationary spin entanglement entropy ridge follows population phase boundaries at small s but lacks the two-branch structure at large s in the sub-Ohmic spin-boson model.