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Contracting projected entangled pair states is average-case hard

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arxiv 1810.00738 v1 pith:EU2I5WX4 submitted 2018-10-01 quant-ph

classification quant-ph
keywords systemsaccurateaverage-casehardnessmany-bodypepsquantumstates
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An accurate calculation of the properties of quantum many-body systems is one of the most important yet intricate challenges of modern physics and computer science. In recent years, the tensor network ansatz has established itself as one of the most promising approaches enabling striking efficiency of simulating static properties of one-dimensional systems and abounding numerical applications in condensed matter theory. In higher dimensions, however, a connection to the field of computational complexity theory has shown that the accurate normalization of the two-dimensional tensor networks called projected entangled pair states (PEPS) is #P-complete. Therefore, an efficient algorithm for PEPS contraction would allow to solve exceedingly difficult combinatorial counting problems, which is considered highly unlikely. Due to the importance of understanding two- and three-dimensional systems the question currently remains: Are the known constructions typical of states relevant for quantum many-body systems? In this work, we show that an accurate evaluation of normalization or expectation values of PEPS is as hard to compute for typical instances as for special configurations of highest computational hardness. We discuss the structural property of average-case hardness in relation to the current research on efficient algorithms attempting tensor network contraction, hinting at a wealth of possible further insights into the average-case hardness of important problems in quantum many-body theory.

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  1. Closing gaps of a quantum advantage with short-time Hamiltonian dynamics

    quant-ph 2019-08 conditional novelty 8.0 of 10

    A constant-time, translation-invariant Hamiltonian quantum simulation architecture is proven to form an approximate unitary 2-design, implying anticoncentration, and its output probabilities are proven #P-hard to comp...

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