Representation and simulation for pyrochlore lattice via Monte Carlo technique

Tensor network contraction is central to problems ranging from many-body physics to computer science. We describe how to approximate tensor network contraction through bond compression on arbitrary graphs. In particular, we introduce a hyperoptimization over the compression and contraction strategy itself to minimize error and cost. We demonstrate that our protocol outperforms both handcrafted contraction strategies in the literature as well as recently proposed general contraction algorithms on a variety of synthetic and physical problems on regular lattices and random regular graphs. We further showcase the power of the approach by demonstrating approximate contraction of tensor networks for frustrated three-dimensional lattice partition functions, dimer counting on random regular graphs, and to access the hardness transition of random tensor network models, in graphs with many thousands of tensors. Published by the American Physical Society 2024

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Representation and simulation for pyrochlore lattice via Monte Carlo technique

Cited by 3 publications

References 13 publications

Phase diagram of the XY Vector Blume–Emery–Griffiths model on a Kagome lattice by Monte Carlo simulation

Phase diagram of the XY Vector Blume–Emery–Griffiths model on a Kagome lattice by Monte Carlo simulation

Effectiveness of the recursive-lattice technique in the investigation of magnetic systems with the pyrochlore structure

Hyperoptimized Approximate Contraction of Tensor Networks with Arbitrary Geometry

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