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.
Matrix product operators, matrix product states, and ab initio density matrix renormalization group algorithms
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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.
Clifford disentanglers classified by Schmidt spectrum action reduce energy errors at fixed bond dimension in MPS simulations of molecules and improve shallow-circuit VQE calculations.
Entanglement structure provides a natural distributed representation for quantum wavefunctions that reduces Hamiltonian applications to local contractions and enables near-linear scaling in simulations.
citing papers explorer
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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.
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Clifford disentanglers for entanglement reduction in molecular electronic structure simulations
Clifford disentanglers classified by Schmidt spectrum action reduce energy errors at fixed bond dimension in MPS simulations of molecules and improve shallow-circuit VQE calculations.
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Entanglement-informed distributed wavefunction approach to scalable quantum many-body systems
Entanglement structure provides a natural distributed representation for quantum wavefunctions that reduces Hamiltonian applications to local contractions and enables near-linear scaling in simulations.