Pre-release
This release is a pre-release and may not be stable for production use.
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--pqlimitto 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
--threadsto leverage multicore processors for faster decoding. - Beam Search: use
--beamto control the trade-off between accuracy and speed. Smaller beam sizes result in faster decoding but potentially lower accuracy. - Beam Climbing: enable
--beam-climbingfor enhanced cost-based decoding. - At most two errors per detector: enable
--at-most-two-errors-per-detectorto improve performance. - Priority Queue limit: use
--pqlimitto limit the size of the priority queue.
Output Formats
- Observable flips output: predictions of logical errors.
- DEM usage frequency output: if
--dem-outis 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 tesseract-decoder-dev@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.
Copyright 2025 Google LLC.
1 maintainer
Metadata
1 maintainer
Release files for tesseract-decoder 0.1.1.dev20250813153142
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 39.1 MB
Release files / tesseract_decoder-0.1.1.dev20250813153142-py312-none-manylinux_2_39_x86_64.manylinux2014_x86_64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py312-none-manylinux_2_39_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 3.4 MB |
| Tags | Linux glibc 2.17+ x86-64 Linux glibc 2.39+ x86-64 Python 3.12 |
|
SHA-256 checksum How to use checksums |
4a766a10161319fafb48f65c62fb75e7d40b00f513332e873227c3e9be4e9690
|
|
BLAKE2b-256 checksum How to use checksums |
3ef39d296b9327cbcfda11e3264635d76d0c27054a32786ef1a56d98000c8fb6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py312-none-manylinux_2_35_x86_64.manylinux2014_x86_64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py312-none-manylinux_2_35_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 3.5 MB |
| Tags | Linux glibc 2.17+ x86-64 Linux glibc 2.35+ x86-64 Python 3.12 |
|
SHA-256 checksum How to use checksums |
849c009283e296c4fd0b45bf977e7934fba81458189e29dcb59acbf05bc77e67
|
|
BLAKE2b-256 checksum How to use checksums |
7f26134ce3d01baf8387a86d05c722f3836d30142ed41a68a79551a192a5b038
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py312-none-macosx_11_0_arm64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py312-none-macosx_11_0_arm64.whl |
|---|---|
| Size | 3.1 MB |
| Tags | Python 3.12 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
01a8713954182e8e081293b32f4ebaf144f4506613dbe1dc0aca3ce32a4de388
|
|
BLAKE2b-256 checksum How to use checksums |
6af48c4f3f754f93c4c0a6f300d8ac14d070041446d76ee765bd0e2a903a0113
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py312-none-macosx_10_13_x86_64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py312-none-macosx_10_13_x86_64.whl |
|---|---|
| Size | 3.0 MB |
| Tags | Python 3.12 macOS 10.13+ x86-64 |
|
SHA-256 checksum How to use checksums |
ba9f97543158f277762f6f03e7f91d62a99c9e0f1598cb617c69385a8dcdd2fe
|
|
BLAKE2b-256 checksum How to use checksums |
6f5a1e7719c80864fa9ee594b2a15a46618a86c18eb2e24a9a35fa1c337cef03
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py311-none-manylinux_2_39_x86_64.manylinux2014_x86_64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py311-none-manylinux_2_39_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 3.4 MB |
| Tags | Linux glibc 2.17+ x86-64 Linux glibc 2.39+ x86-64 Python 3.11 |
|
SHA-256 checksum How to use checksums |
d8c0a8b09573d8712b6165c78409ceb2c3f68f7e6df84cedc76b813b59b72416
|
|
BLAKE2b-256 checksum How to use checksums |
33a0f99a528589c40d205ea17d288ec0e839e12180b4b579dfb22626453cea06
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py311-none-manylinux_2_35_x86_64.manylinux2014_x86_64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py311-none-manylinux_2_35_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 3.5 MB |
| Tags | Linux glibc 2.17+ x86-64 Linux glibc 2.35+ x86-64 Python 3.11 |
|
SHA-256 checksum How to use checksums |
76c516614a746b12825ff46e78e321bccde4c0d562d359adb34119d438f477b3
|
|
BLAKE2b-256 checksum How to use checksums |
5f43264112d658a1aad03abf64a2275507ac6f878137520ad073b79e2428828a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py311-none-macosx_11_0_arm64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py311-none-macosx_11_0_arm64.whl |
|---|---|
| Size | 3.1 MB |
| Tags | Python 3.11 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
25be2a613b15846ef2f6e715fb6b85b20a3dc4a61f48534a306e6740ea6b557f
|
|
BLAKE2b-256 checksum How to use checksums |
04d3860cd0679a755ce4720a74e93322d5b7ea8462c322e34c08a4637ac13baf
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py311-none-macosx_10_13_x86_64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py311-none-macosx_10_13_x86_64.whl |
|---|---|
| Size | 3.0 MB |
| Tags | Python 3.11 macOS 10.13+ x86-64 |
|
SHA-256 checksum How to use checksums |
dd124abf44b582c17410126296b7ef97d4f2371440981ef8557898a8c3b3e8f0
|
|
BLAKE2b-256 checksum How to use checksums |
3f659d35d551e3cc35aced6e4de1279e5b9d954decafe50cc96f4b61d1b787e5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py310-none-manylinux_2_39_x86_64.manylinux2014_x86_64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py310-none-manylinux_2_39_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 3.4 MB |
| Tags | Linux glibc 2.17+ x86-64 Linux glibc 2.39+ x86-64 Python 3.10 |
|
SHA-256 checksum How to use checksums |
efe7185ef8dbf67c4cb10031e8a836262dc2de34e70f195efffd4444ca78bc20
|
|
BLAKE2b-256 checksum How to use checksums |
ef8f140b343ed694ef2b7211f9b4b08f9b05669d209ab36d3b4f8317cfb3f75b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py310-none-manylinux_2_35_x86_64.manylinux2014_x86_64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py310-none-manylinux_2_35_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 3.5 MB |
| Tags | Linux glibc 2.17+ x86-64 Linux glibc 2.35+ x86-64 Python 3.10 |
|
SHA-256 checksum How to use checksums |
eee6b8ba1d447eea2df2cd8498fe3e557679899f8fef907125db47c465d59f71
|
|
BLAKE2b-256 checksum How to use checksums |
6e2fe8c02fc7b98e098118681e6090ad469f510693ad89b83a30731bdc7bcf55
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py310-none-macosx_11_0_arm64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py310-none-macosx_11_0_arm64.whl |
|---|---|
| Size | 3.1 MB |
| Tags | Python 3.10 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
780d9c7a1900cb89d67c60a67b0698e864daeb5d8264581da8b2189466c0265a
|
|
BLAKE2b-256 checksum How to use checksums |
f70e1a341d8e2ea25efac6bb77127825cfc1eeff39ec6e1ebc363770c9e5fa2f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
Release files / tesseract_decoder-0.1.1.dev20250813153142-py310-none-macosx_10_13_x86_64.whl
| Download URL | tesseract_decoder-0.1.1.dev20250813153142-py310-none-macosx_10_13_x86_64.whl |
|---|---|
| Size | 3.0 MB |
| Tags | Python 3.10 macOS 10.13+ x86-64 |
|
SHA-256 checksum How to use checksums |
704f285a5cfbb7437885203dedd62761d2764e93de7d1a243b8a050759c52b2c
|
|
BLAKE2b-256 checksum How to use checksums |
a562a5e8780e02e703ab9389dda3ffdc518e2ec18985920472a1f6a5e403f9bb
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.9.23
|
