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Tensor networks are powerful factorization techniques which reduce resource requirements for numerically simulating principal quantum many-body systems and algorithms. The computational complexity of a tensor network simulation depends on the tensor ranks and the order in which they are contracted. Unfortunately, computing optimal contraction sequences (orderings) in general is known to be a computationally difficult (NP-complete) task. In 2005, Markov and Shi showed that optimal contractiondoi:10.1371/journal.pone.0207827 pmid:30562341 pmcid:PMC6298732 arXiv:1807.04599v1 fatcat:iyr23dlkjbehdmr4lhdeltz5qu