Contracting tensor networks with potentially complex geometry is a useful task across many fields including quantum circuit simulation. The computational cost of this is extraordinarily sensitive to the so-called contraction tree, and here we describe a method of building these based on hyper-graph partitioning that appears to be close to optimal. Driven by this we also introduce a set of tensor network simplifications that aim to make contraction easier - and which turn out to surprisingly powerful on their own. Finally we touch on extending these ideas to approximate contraction.
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