Performance Estimator of Codes On Surfaces (PECOS) is a library/framework dedicated to the study, development, and evaluation of quantum error-correction protocols. It also offers tools for the study and evaluation of hybrid quantum/classical compute execution models for NISQ algorithms and beyond.

Initially conceived and developed in 2014 to verify lattice-surgery procedures presented in arXiv:1407.5103 and released publicly in 2018, PECOS filled the gap in the QEC/QC tools available at that time. Over the years, it has grown into a framework for studying general QECCs and hybrid computation.

Features

• Quantum Error-Correction Tools: Advanced tools for studying quantum error-correction protocols and error models.
• Hybrid Quantum/Classical Execution: Evaluate advanced hybrid compute models, including support for classical compute, calls to Wasm VMs, conditional branching, and more.
• Fast Simulation: Leverages a fast stabilizer simulation algorithm.
• Multi-language extensions: Core functionalities implemented via Rust for performance and safety. Additional add-ons and extension support in C/C++ via Cython.

Getting Started

Explore the capabilities of PECOS by delving into the documentation.

Repository Structure

PECOS now consists of multiple interconnected components:

• /python/: Contains Python packages
  • /python/quantum-pecos/: Main Python package (imports as pecos)
  • /python/pecos-rslib/: Python package with Rust extensions that utilize the pecos crate
• /crates/: Contains Rust crates
  • /crates/pecos/: Main Rust crate that collects the functionality of the other crates into one library
  • /crates/pecos-core/: Core Rust functionalities
  • /crates/pecos-qsims/: A collection of quantum simulators
  • /crates/pecos-qec/: Rust code for analyzing and exploring quantum error correction (QEC)
  • /crates/pecos-python/: Rust code for Python extensions
  • /crates/benchmarks/: A collection of benchmarks to test the performance of the crates

You may find most of these crates in crates.io if you wish to utilize only a part of PECOS, e.g., the simulators.

Versioning

We follow semantic versioning principles. However, before version 1.0.0, the MAJOR.MINOR.BUG format sees the roles of MAJOR and MINOR shifted down a step. This means potential breaking changes might occur between MINOR increments, such as moving from versions 0.1.0 to 0.2.0.

All Python packages and all Rust crates will have the same version amongst their respective languages; however, Python and Rust versioning will differ.

Latest Development

Stay updated with the latest developments on the PECOS Development branch.

Installation

Python Package

To install the main Python package for general usage:

pip install quantum-pecos

This will install both quantum-pecos and its dependency pecos-rslib.

For optional dependencies:

pip install quantum-pecos[all]

NOTE: The quantum-pecos package is imported like: import pecos and not import quantum_pecos.

NOTE: To install pre-releases (the latest development code) from pypi you may have to specify the version you are interested like so (e.g., for version 0.6.0.dev5):

pip install quantum-pecos==0.6.0.dev5

NOTE: Certain simulators have special requirements and are not installed by the command above. Installation instructions for these are provided here.

Rust Crates

To use PECOS in your Rust project, add the following to your Cargo.toml:

[dependencies]
pecos = "0.x.x"  # Replace with the latest version

Development Setup

If you are interested in editing or developing the code in this project, see this development documentation to get started.

Simulators with special requirements

Certain simulators from pecos.simulators require external packages that are not installed by pip install .[all].

• QuEST is installed along with the python package pyquest when calling pip install .[all]. However, it uses 64-bit float point precision by default, and if you wish to make use of 32-bit float point precision you will need to follow the installation instructions provided by the developers here.
• CuStateVec requires a Linux machine with an NVIDIA GPU (see requirements here). PECOS' dependencies are specified in the [cuda] section of pyproject.toml, however, installation via pip is not reliable. The recommended method of installation is via conda, as discussed here. Note that there might be conflicts between conda and venv; if you intend to use CuStateVec, you may follow the installation instructions for PECOS within a conda environment without involving the venv commands.
• MPS uses pytket-cutensornet (see repository) and can be installed via pip install .[cuda]. These simulators use NVIDIA GPUs and cuQuantum. Unfortunately, installation of cuQuantum does not currently work via pip. Please follow the instructions specified above for CuStateVec to install cuQuantum.

Uninstall

To uninstall:

pip uninstall quantum-pecos

Citing

For publications utilizing PECOS, kindly cite PECOS such as:

@misc{pecos,
 author={Ciar\'{a}n Ryan-Anderson},
 title={PECOS: Performance Estimator of Codes On Surfaces},
 publisher = {GitHub},
 journal = {GitHub repository},
 howpublished={\url{https://github.com/PECOS-packages/PECOS}},
 URL = {https://github.com/PECOS-packages/PECOS},
 year={2018}
}

And/or the PhD thesis PECOS was first described in:

@phdthesis{crathesis,
 author={Ciar\'{a}n Ryan-Anderson},
 school = {University of New Mexico},
 title={Quantum Algorithms, Architecture, and Error Correction},
 journal={arXiv:1812.04735},
 URL = {https://digitalrepository.unm.edu/phyc_etds/203},
 year={2018}
}

You can also use the Zenodo DOI, which would result in a bibtex like:

@software{pecos_[year],
  author       = {Ciar\'{a}n Ryan-Anderson},
  title        = {PECOS-packages/PECOS: [version]]},
  month        = [month],
  year         = [year],
  publisher    = {Zenodo},
  version      = {[version]]},
  doi          = {10.5281/zenodo.13700104},
  url          = {https://doi.org/10.5281/zenodo.13700104}
}

License

This project is licensed under the Apache-2.0 License - see the LICENSE and NOTICE files for details.

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