Size consistency of tensor network methods for quantum many-body systems
classification
❄️ cond-mat.str-el
quant-ph
keywords
networktensorconsistencydemonstratemany-bodymethodsproblemquantum
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Recently developed tensor network methods demonstrate great potential for addressing the quantum many-body problem, by constructing variational spaces with polynomially, instead of exponentially, scaled parameters. Constructing such an efficient tensor network, and thus the variational space, is a subtle problem and the main obstacle of the method. We demonstrate the necessity of size consistency in tensor network methods for their success in addressing the quantum many-body problem. We further demonstrate that size consistency is independent of the entanglement criterion, thus providing a general and tight constraint to construct the tensor network method.
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