期刊
SIAM JOURNAL ON COMPUTING
卷 39, 期 7, 页码 3089-3121出版社
SIAM PUBLICATIONS
DOI: 10.1137/080739379
关键词
tensor networks; quantum algorithms; statistical mechanical models
We present a quantum algorithm that additively approximates the value of a tensor network to a certain scale. When combined with existing results, this provides a complete problem for quantum computation. The result is a simple new way of looking at quantum computation in which unitary gates are replaced by tensors and time is replaced by the order in which the tensor network is swallowed. We use this result to derive new quantum algorithms that approximate the partition function of a variety of classical statistical mechanical models, including the Potts model.
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