US 12,481,907 B2
Methods and systems for tensor network contraction based on hypergraph decomposition and parameter optimization
Jiachen Huang, Hangzhou (CN); and Jianxin Chen, Hangzhou (CN)
Assigned to Alibaba Group Holding Limited, Grand Cayman (KY)
Filed by Alibaba Group Holding Limited, Grand Cayman (KY)
Filed on Apr. 23, 2021, as Appl. No. 17/238,870.
Claims priority of provisional application 63/015,116, filed on Apr. 24, 2020.
Prior Publication US 2021/0334690 A1, Oct. 28, 2021
Int. Cl. G06N 20/00 (2019.01); G06F 18/214 (2023.01); G06F 18/2323 (2023.01); G06N 10/60 (2022.01); G06N 10/80 (2022.01)
CPC G06N 10/60 (2022.01) [G06F 18/214 (2023.01); G06F 18/2323 (2023.01); G06N 10/80 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method implemented by a computing host, the method comprising:
obtaining a plurality of tensor nodes associated with a tensor network that is defined according to states of a quantum circuit 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 using a multi-partite decomposition algorithm associated with a first set of parameters;
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 using a bipartition decomposition algorithm associated with a second set of parameters, the iteratively decomposing including:
determining that a first index of one or more first indices associated with a first node has a same dimension as a second index of one or more second indices associated with a second node in at least one of the plurality of next-tier sub-graphs;
contracting the first node and the second node to form a third node;
computing a count of nodes in the at least one of the plurality of next-tier sub-graphs; and
determining the count of nodes is less than the pre-set threshold;
dynamically optimizing the first set of parameters and/or the second set of parameters to generate a plurality of contraction trees associated with the tensor network that simulates the quantum circuit in a quantum computing simulation platform based on the plurality of next-tier sub-graphs; and
finding an optimal contraction tree from the plurality of contraction trees that reduces computation time and/or storage space to enable a simulation of quantum computations without fully simulating a full dimensional space of the quantum computations.