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Tesseract Decoder

A Search-Based Decoder for Quantum Error Correction.

C++

Installation – Usage – Paper – Help – Citation – Contact

Tesseract is a Most Likely Error decoder designed for Low Density Parity Check (LDPC) quantum error-correcting codes. It applies pruning heuristics and manifold orientation techniques during a search over the error subsets to identify the most likely error configuration consistent with the observed syndrome. Tesseract achieves significant speed improvements over traditional integer programming-based decoders while maintaining comparable accuracy at moderate physical error rates.

We tested the Tesseract decoder for:

Surface codes
Color codes
Bivariate-bicycle codes
Transversal CNOT protocols for surface codes

Features

A* search: deploys A* search while running a Dijkstra algorithm with early stop for high performance.
Stim and DEM Support: processes Stim circuit files and Detector Error Model (DEM) files with arbitrary error models. Zero-probability error instructions are automatically removed when a DEM is loaded.
Parallel Decoding: uses multithreading to accelerate the decoding process, making it suitable for large-scale simulations.
Efficient Beam Search: implements a beam search algorithm to minimize decoding cost and enhance efficiency. Sampling and Shot Range Processing: supports sampling shots from circuits. When a detection error model is provided without an accompanying circuit, Tesseract requires detection events from files using --in. The decoder can also process specific shot ranges for flexible experiment setups.
Detailed Statistics: provides comprehensive statistics output, including shot counts, error counts, and processing times.
Heuristics: includes flexible heuristic options: --beam, --det-penalty, --beam-climbing, --no-revisit-dets, --at-most-two-errors-per-detector, and --pqlimit to improve performance while maintaining a low logical error rate. To learn more about these options, use ./bazel-bin/src/tesseract --help
Visualization tool: open the viz directory in your browser to view decoding results. See viz/README.md for instructions on generating the visualization JSON.

Installation

Tesseract relies on the following external libraries:

argparse: For command-line argument parsing.
nlohmann/json: For JSON handling (used for statistics output).
Stim: For quantum circuit simulation and error model handling.

Build Instructions

Tesseract uses Bazel as its build system. To build the decoder:

bazel build src:all

Running Tests

Unit tests are executed with Bazel. Run the quick test suite using:

bazel test //src:all

By default the tests use reduced parameters and finish in under 30 seconds. To run a more exhaustive suite with additional shots and larger distances, set:

TESSERACT_LONG_TESTS=1 bazel test //src:all

Usage

The file tesseract_main.cc provides the main entry point for Tesseract Decoder. It can decode error events from Stim circuits, DEM files, and pre-existing detection event files.

Basic Usage:

./tesseract --circuit CIRCUIT_FILE.stim --sample-num-shots N --print-stats

To decode pre-generated detection events, provide the input file using --in SHOTS_FILE --in-format FORMAT.

Example with Advanced Options:

./tesseract \
        --pqlimit 1000000 \
        --at-most-two-errors-per-detector \
        --det-order-seed 232852747 \
        --circuit circuit_file.stim \
        --sample-seed 232856747 \
        --sample-num-shots 10000 \
        --threads 32 \
        --print-stats \
        --beam 23 \
        --num-det-orders 1 \
        --shot-range-begin 582 \
        --shot-range-end 583

Example Usage

Sampling Shots from a Circuit:

./tesseract --circuit surface_code.stim --sample-num-shots 1000 --out predictions.01 --out-format 01

Using a Detection Event File:

./tesseract --in events.01 --in-format 01 --dem surface_code.dem --out decoded.txt

Using a Detection Event File and Observable Flips:

./tesseract --in events.01 --in-format 01 --obs_in obs.01 --obs-in-format 01 --dem surface_code.dem --out decoded.txt

Tesseract supports reading and writing from all of Stim's standard output formats.

Performance Optimization

Here are some tips for improving performance:

Parallelism over shots: increase --threads to leverage multicore processors for faster decoding.
Beam Search: use --beam to control the trade-off between accuracy and speed. Smaller beam sizes result in faster decoding but potentially lower accuracy.
Beam Climbing: enable --beam-climbing for enhanced cost-based decoding.
At most two errors per detector: enable --at-most-two-errors-per-detector to improve performance.
Priority Queue limit: use --pqlimit to limit the size of the priority queue.

Output Formats

Observable flips output: predictions of logical errors.
DEM usage frequency output: if --dem-out is specified, outputs estimated error frequencies.
Statistics output: includes number of shots, errors, low confidence shots, and processing time.

Help

Do you have a feature request or want to report a bug? Open an issue on GitHub to report it!
Do you have a code contribution? Read our contribution guidelines, then open a pull request!

We are committed to providing a friendly, safe, and welcoming environment for all. Please read and respect our Code of Conduct.

Citation

When publishing articles or otherwise writing about Tesseract Decoder, please cite the following:

@misc{beni2025tesseractdecoder,
    title={Tesseract: A Search-Based Decoder for Quantum Error Correction},
    author = {Aghababaie Beni, Laleh and Higgott, Oscar and Shutty, Noah},
    year={2025},
    eprint={2503.10988},
    archivePrefix={arXiv},
    primaryClass={quant-ph},
    doi = {10.48550/arXiv.2503.10988},
    url={https://arxiv.org/abs/2503.10988},
}

Contact

For any questions or concerns not addressed here, please email quantum-oss-maintainers@google.com.

Disclaimer

Tesseract Decoder is not an officially supported Google product. This project is not eligible for the Google Open Source Software Vulnerability Rewards Program.

Metadata

Release files for tesseract-decoder 0.1.1.dev20250715222532

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Built distributions (wheels)

Table of built distributions (wheels) for tesseract-decoder 0.1.1.dev20250715222532
File	Interpreter	ABI	Platform	Reset
tesseract_decoder-0.1.1.dev20250715222532-py312-py312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3.3 MB Jul 15, 2025	Python 3.12	py312	Linux glibc 2.17+ x86-64	Details
tesseract_decoder-0.1.1.dev20250715222532-py312-py312-macosx_10_13_x86_64.whl 2.9 MB Jul 15, 2025	Python 3.12	py312	macOS 10.13+ x86-64	Details
tesseract_decoder-0.1.1.dev20250715222532-py311-py311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3.3 MB Jul 15, 2025	Python 3.11	py311	Linux glibc 2.17+ x86-64	Details
tesseract_decoder-0.1.1.dev20250715222532-py311-py311-macosx_10_13_x86_64.whl 2.9 MB Jul 15, 2025	Python 3.11	py311	macOS 10.13+ x86-64	Details
tesseract_decoder-0.1.1.dev20250715222532-py310-py310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3.3 MB Jul 15, 2025	Python 3.10	py310	Linux glibc 2.17+ x86-64	Details
tesseract_decoder-0.1.1.dev20250715222532-py310-py310-macosx_10_13_x86_64.whl 2.9 MB Jul 15, 2025	Python 3.10	py310	macOS 10.13+ x86-64	Details

Total release size: 18.7 MB

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Download URL	tesseract_decoder-0.1.1.dev20250715222532-py311-py311-macosx_10_13_x86_64.whl
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Download URL	tesseract_decoder-0.1.1.dev20250715222532-py310-py310-macosx_10_13_x86_64.whl
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