{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"Sinter performs fast Monte Carlo sampling of quantum error correction circuits, using stim to generate samples and decoders like pymatching to predict logical errors from detector data.","skillfed_tags":["quantum-computing","error-correction","monte-carlo-simulation"],"use_cases":["Benchmark surface code implementations by sampling circuits at various physical error rates and measuring resulting logical error rates.","Compare decoder performance on the same set of QEC circuits by running collection with different decoders and analyzing CSV output.","Automate large-scale QEC sampling campaigns on a multi-core workstation, resuming interrupted runs without data loss via checkpoint files.","Generate plots of logical error probability versus physical error rate to visualize code performance across parameter sweeps.","Prototype and validate new quantum error correction codes by generating custom Stim circuits and collecting statistics via the Python API."],"what_it_does":"Sinter is a Python library and command-line tool for sampling quantum error correction circuits and measuring logical error rates. It takes Stim circuits annotated with noise, detectors, and logical observables, uses Stim to generate samples, applies a decoder to predict whether logical errors occurred from detector data, and records success and failure rates. The tool uses Python multiprocessing to parallelize sampling across multiple CPU cores, dynamically allocates additional samples to circuits that need them based on user-specified targets (such as a minimum error count), saves results in CSV format, and provides basic plotting utilities for visualization.\n\nSinter is designed for single-machine benchmarking of quantum error correction codes. It scales well across machines with varying core counts (tested on 2, 4, and 96 core systems) and achieves good resource utilization when batch sizes are tuned appropriately. The library exposes both a Python API for programmatic use and a command-line interface for batch processing of circuit files, making it suitable for researchers prototyping and analyzing error correction schemes.","worth_installing":"Yes, with conditions. Install if you are actively researching or benchmarking quantum error correction codes and have stim and a decoder like pymatching available. The high install friction (four runtime dependencies) and the requirement to structure code around multiprocessing make it less suitable for casual exploration, but the active maintenance, permissive license, and no known vulnerabilities make it safe for production research use. Verify that pymatching or your chosen decoder is available before committing to the full stack."},"id":"sinter","links":{"html":"https://skillfed.io/packages/sinter","md":"https://skillfed.io/packages/sinter.md","pypi":"https://pypi.org/project/sinter/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-22","license_spdx":null,"license_treatment":"permissive","name":"sinter","python_support":"supports_current","summary":"Samples stim circuits and decodes them using pymatching."},"popularity":{"monthly_downloads":92090,"position":13481,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.16.0"}