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This research introduces Leg-Tied Tensor Network States (LETTA), a novel framework that shares physical legs among local tensors to directly encode long-range correlations beyond standard virtual bonds. By combining this structure with an MPS backbone, the method achieves accuracy comparable to much larger models while using significantly fewer variational parameters for complex quantum spin systems. This advancement represents a meaningful step forward in optimizing tensor network algorithms for simulating strongly correlated quantum materials.
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Leg-Tied Tensor Network States: Entanglement Beyond Virtual Bonds

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arXiv:2609.30101v1 Announce Type: new Abstract: We introduce a class of tensor-network states in which physical legs are shared among local tensors, termed leg-tied tensor ans\"atze (LETTA). Physical leg ties encode long-range correlations directly, while a virtual matrix product state (MPS) backbone retains short-range multipartite entanglement. The linear virtual backbone allows us to develop a deterministic density matrix renormalization group-like variational optimization algorithm using exact contractions over the active tie-boundary sets and local minimization. We demonstrate the advantages of LETTA for the two-dimensional frustrated $J_1$--$J_2$ Heisenberg model and the three-dimensional transverse-field Ising model. Our results show that LETTA is substantially more accurate than same-bond-dimension MPS calculations and can typically reach the accuracy of much larger MPS calculations using one order of magnitude fewer variational parameters. LETTA thus opens the door for explicitly correlated tensor-network states that can encode long-range correlation beyond virtual bonds.

Abstract:We introduce a class of tensor-network states in which physical legs are shared among local tensors, termed leg-tied tensor ansätze (LETTA). Physical leg ties encode long-range correlations directly, while a virtual matrix product state (MPS) backbone retains short-range multipartite entanglement. The linear virtual backbone allows us to develop a deterministic density matrix renormalization group-like variational optimization algorithm using exact contractions over the active tie-boundary sets and local minimization. We demonstrate the advantages of LETTA for the two-dimensional frustrated $J_1$--$J_2$ Heisenberg model and the three-dimensional transverse-field Ising model. Our results show that LETTA is substantially more accurate than same-bond-dimension MPS calculations and can typically reach the accuracy of much larger MPS calculations using one order of magnitude fewer variational parameters. LETTA thus opens the door for explicitly correlated tensor-network states that can encode long-range correlation beyond virtual bonds.

Quantum Physics (quant-ph); Strongly Correlated Electrons (cond-mat.str-el)

arXiv:2609.30101 [quant-ph]

(or arXiv:2609.30101v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.30101 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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