bqa 0.1.2

A framework for tensor-network–based quantum annealing simulation powered by belief propagation.

What is it?

This is a package for large scale tensor-networks-based simulation of quantum annealing. It uses belief propagation based approximate inference (see https://arxiv.org/abs/2306.17837, https://arxiv.org/abs/2409.12240, https://arxiv.org/abs/2306.14887) as an engine. This implementation introduces a compilation step that classifies graph nodes by degree, groups the corresponding tensors into batched representations, groups the associated messages, enabling massively parallel belief propagation and related subroutines easelly deployable on a GPU.

How to install?

1. Clone this repo;
2. Run pip install . from the clonned repo under your python environment.

To validate the computation results, some examples and tests rely on an exact quantum circuit simulator available at https://github.com/LuchnikovI/qem. To install it, follow the steps below:

1. Clone the repo https://github.com/LuchnikovI/qem;
2. Install rust (see https://rust-lang.org/tools/install/);
3. Install maturin by running pip install maturin .;
4. Run pip install . from the clonned repo under your python environment.

How to use?

This package exposes a single entry point, run_qa, which executes the full workflow. It accepts a single argument which is a Python dictionary that fully specifies the quantum annealing task. This dictionary serves as a configuration or DSL and can be directly deserialized from JSON or other formats. For a concrete example of the configuration, see ./examples/small_ibm_heavy_hex.py.