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The area law of entanglement entropy is known to heavily constrain the accessible system sizes in tensor network method simulations of 2D quantum magnetism. The situation is further complicated by the presence of geometrical frustration, which enhances quantum fluctuations and ground state degeneracy, leading to increased computational difficulty. We recently demonstrated that the Density Matrix Renormalization Group algorithm is enhanced by a judicious choice of the bijective mapping for the Matrix Product State ansatz of the square lattice antiferromagnet [1]. In particular, we showed that any fractal path mapping, such as the Hilbert curve, improves convergence of the algorithm with respect to bond dimension over the conventional row-by-row mapping. We show that such convergence enhancement is also seen for the triangular antiferromagnet, allowing for accurate determination of the ground state energy and magnetization.
[1] arXiv:2507.11820
| Field of Condensed Matter | Magnetism |
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