For PEPS states with loop-decay, BP with cluster corrections approximates local observables exponentially accurately, and loop-decay necessarily implies exponential decay of connected correlations, ruling out BP at critical points.
Schollw¨ ock, The density-matrix renormalization group in the age of matrix product states, Annals of Physics 326, 96–192 (2011)
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A tensor network algorithm computes momentum-resolved spectral functions for large non-periodic super-moiré systems by mapping tight-binding problems to solvable quantum many-body simulations using kernel polynomial methods and quantum Fourier transforms.
New low-bond MPO construction, entanglement-aware MPS optimization, and Gutzwiller parameter tuning enable transcorrelated DMRG on 12x12 lattices, cutting ground-state energy errors by 3x to 17x versus standard DMRG at equal effort.
SNT merges SV and PEC for subspace-tailored error mitigation in Trotterized FHM simulations, mapping out optimal combinations by hardware quality and shot budget while quantifying when noisy devices could surpass classical methods.
The paper proposes variational decision diagrams (VDDs) for quantum state representation in QML and reports successful training without barren plateaus on transverse-field Ising and Heisenberg Hamiltonians.
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Belief Propagation and Tensor Network Expansions for Many-Body Quantum Systems: Rigorous Results and Fundamental Limits
For PEPS states with loop-decay, BP with cluster corrections approximates local observables exponentially accurately, and loop-decay necessarily implies exponential decay of connected correlations, ruling out BP at critical points.
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Tensor network approach to momentum-resolved spectroscopy in non-periodic super-moir\'e systems
A tensor network algorithm computes momentum-resolved spectral functions for large non-periodic super-moiré systems by mapping tight-binding problems to solvable quantum many-body simulations using kernel polynomial methods and quantum Fourier transforms.
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Scaling up the transcorrelated density matrix renormalization group
New low-bond MPO construction, entanglement-aware MPS optimization, and Gutzwiller parameter tuning enable transcorrelated DMRG on 12x12 lattices, cutting ground-state energy errors by 3x to 17x versus standard DMRG at equal effort.
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Near-Term Fermionic Simulation with Subspace Noise Tailored Quantum Error Mitigation
SNT merges SV and PEC for subspace-tailored error mitigation in Trotterized FHM simulations, mapping out optimal combinations by hardware quality and shot budget while quantifying when noisy devices could surpass classical methods.
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Variational decision diagrams for quantum-inspired machine learning applications
The paper proposes variational decision diagrams (VDDs) for quantum state representation in QML and reports successful training without barren plateaus on transverse-field Ising and Heisenberg Hamiltonians.