OMEinsum.jl is a pure Julia package for tensor network computation, which has been used in various projects, including
GenericTensorNetworks.jl for solving combinatorial optimization problems,
YaoToEinsum.jl for simulating large scale quantum circuit and
TensorInference.jl for Bayesian inference.
Unlike other tensor contraction packages such as ITensors.jl and TensorOperations.jl, it is designed for large scale tensor networks with arbitrary topology. The key feature of OMEinsum.jl is that it can automatically optimize the contraction order of a tensor network. Related features are implemented in OMEinsumContractionOrders.jl.
We are looking for a student to work on the following tasks:
Implement a better contraction order optimizer based on Tamaki's algorithm.
Implement a hyper-graph visualization tool based on arXiv:2308.05043
Port the contraction order optimizers to TensorOperations.jl
Recommended skills: familiarity with tensor networks, graph theory and high performance computing.
Expected results:
new features added to the package OMEinsumContractionOrders.jl along with tests and relevant documentation.
a new package about hyper-graph visualization, and relevant feature added to OMEinsum.jl.
a pull request to TensorOperations.jl for better contraction order optimization.
Mentors: Jin-Guo Liu, Jutho Haegeman and Lukas Devos
Project difficulty: Medium to Hard
Project length: 350 hrs
Contact: feel free to ask questions via email or the Julia slack (user name: JinGuo Liu).