tesseract-decoder 0.1.1.dev20250819194213

A search-based decoder for quantum error correction (QEC).

Tesseract Decoder

A Search-Based Decoder for Quantum Error Correction.

Installation – Usage – Python Interface – 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.

Python Interface

Full Python wrapper documentation

This repository contains the C++ implementation of the Tesseract quantum error correction decoder, along with a Python wrapper. The Python wrapper/interface exposes the decoding algorithms and helper utilities, allowing Python users to leverage this high-performance decoding algorithm.

The following example demonstrates how to create and use the Tesseract decoder using the Python interface.

from tesseract_decoder import tesseract
import stim
import numpy as np


# 1. Define a detector error model (DEM)
dem = stim.DetectorErrorModel("""
    error(0.1) D0 D1 L0
    error(0.2) D1 D2 L1
    detector(0, 0, 0) D0
    detector(1, 0, 0) D1
    detector(2, 0, 0) D2
""")

# 2. Create the decoder configuration
config = tesseract.TesseractConfig(dem=dem, det_beam=50)

# 3. Create a decoder instance
decoder = config.compile_decoder()

# 4. Simulate detection events
syndrome = [0, 1, 1]

# 5a. Decode to observables
flipped_observables = decoder.decode(syndrome)
print(f"Flipped observables: {flipped_observables}")

# 5b. Alternatively, decode to errors
decoder.decode_to_errors(np.where(syndrome)[0])
predicted_errors = decoder.predicted_errors_buffer
# Indices of predicted errors
print(f"Predicted errors indices: {predicted_errors}")
# Print properties of predicted errors
for i in predicted_errors:
    print(f"    {i}: {decoder.errors[i]}")

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 [e-mail].

Disclaimer

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

Copyright 2025 Google LLC.