bqa 0.2.0

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

What is it?

This package provides large-scale tensor-network-based emulator of quantum annealing, powered by belief-propagation-based inference. 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. This design enables scaling up to 1,000,000 for a range of nontrivial problem instances.

How to use it?

There is a wiki page with the documentation.

How to install?

1. Clone this repo;
2. Run poetry env activate && poetry install from the root of the clonned repo.

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 run benchmarks against MQLib?

First, one need to install an MQLib wrapper awailable here, follow the instruction of README there. Now one can execute scripts in ./benchmarks_against_mqlib, every script saves a result into a separate directory with time stamp.

NumPy backend

To run quantum annealing emulation using numpy backend, one does not need any extra configuration steps. One can control the precision of the numpy backend by setting the environment variable export BQA_PECISION=single for the single precision and export BQA_PRECISION=double for the double precision.

CuPy backend

To use cupy backend one needs to install cupy sepraratelly since it is not in the dependancies list. One also need cuTENSOR to enable fast tensor contraction. The precision of the cupy backend is always single.