NN-fTNS enhance fermionic tensor networks with neural parametrization to improve expressivity and achieve order-of-magnitude better energies than pure fTNS on Hubbard models while maintaining linear scaling.
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A QR-based CTMRG variant accelerates iPEPS contractions by up to two orders of magnitude on GPUs with no accuracy loss for the Heisenberg and J1-J2 models.
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Neuralized Fermionic Tensor Networks for Quantum Many-Body Systems
NN-fTNS enhance fermionic tensor networks with neural parametrization to improve expressivity and achieve order-of-magnitude better energies than pure fTNS on Hubbard models while maintaining linear scaling.
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Accelerating two-dimensional tensor network contractions using QR decompositions
A QR-based CTMRG variant accelerates iPEPS contractions by up to two orders of magnitude on GPUs with no accuracy loss for the Heisenberg and J1-J2 models.