A simulated annealing refinement of tensor network partitionings for distributed contraction lowers estimated computational and memory cost by about 8x on average versus naive partitioning on MQT Bench circuits.
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Optimizing Tensor Network Partitioning using Simulated Annealing
A simulated annealing refinement of tensor network partitionings for distributed contraction lowers estimated computational and memory cost by about 8x on average versus naive partitioning on MQT Bench circuits.