Methods and systems for tensor network contraction based on hypergraph decomposition and parameter optimization

A system comprising:

• one or more processors, and
• memory communicatively coupled to the one or more processors, the memory storing computer-executable modules executable by the one or more processors that, when executed by the one or more processors, perform actions including:
• obtaining a plurality of tensor nodes associated with a tensor network and a plurality of indices respectively associated with the plurality of tensor nodes;
• generating a graph associated with the tensor network, wherein the plurality of tensor nodes correspond to a plurality of vertices of the graph and the plurality of indices correspond to a plurality of edges of the graph, respectively;
• decomposing the graph into a plurality of sub-graphs;
• and
• for each sub-graph of the plurality of sub-graphs, iteratively decomposing a current sub-graph into a plurality of next-tier sub-graphs until a size of each of the plurality of next-tier sub-graphs is less than a pre-set threshold.

A computer-readable storage medium storing computer-readable instructions executable by one or more processors of a video compression system, that when executed by the one or more processors, cause the one or more processors to perform actions comprising:

• obtaining a plurality of tensor nodes associated with a tensor network and a plurality of indices respectively associated with the plurality of tensor nodes;
• generating a graph associated with the tensor network, wherein the plurality of tensor nodes correspond to a plurality of vertices of the graph and the plurality of indices correspond to a plurality of edges of the graph, respectively;
• decomposing the graph into a plurality of sub-graphs;
• and
• for each sub-graph of the plurality of sub-graphs, iteratively decomposing a current sub-graph into a plurality of next-tier sub-graphs until a size of each of the plurality of next-tier sub-graphs is less than a pre-set threshold.