aegis-qec 1.0.22

Research-grade, hardware-aware QEC stack for surface codes.

Aegis – Hardware-Aware Quantum Error Correction Toolkit

Aegis is a modular toolkit for simulated quantum error correction with a focus on clarity, performance, and smooth automation from local dev to PyPI release.

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Features

• Decoders: Greedy (+ OSD fallback) and MWPM
• Reusable decoding graphs for performance experiments
• Metrics & threshold plots (CSV/PNG)
• Ready-to-use CLI: aegis-run, aegis-metrics, aegis-threshold, aegis-export-header
• Quality gate: CI on Win/Ubuntu (Py 3.9/3.12), pre-commit (black + ruff), Dependabot
• Releases: Tagged publishes to TestPyPI / PyPI via GitHub Actions

Requires Python 3.9+.

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Installation

Minimal runtime

pip install aegis-qec==1.0.6

Full (plots & dev extras)

pip install "aegis-qec[full]==1.0.6"

Extras:

• full → matplotlib, pyarrow, pytest
• mwpm → networkx (already required by core)

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Quickstart (CLI)

Outputs are written to ./out/ by default.

# Fast sample decode
aegis-run
# -> "Decode complete. X(avg_cost)=..., Z(avg_cost)=..."

# Metrics + plot
aegis-metrics
# -> out/metrics.csv, out/metrics.png

# Threshold plot
aegis-threshold
# -> out/threshold.png

# Export a LUT header (example: GKP)
aegis-export-header
# -> out/gkp_lut.h

Windows PowerShell one-liner:

aegis-run; aegis-metrics; aegis-threshold; aegis-export-header

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Quickstart (Python)

import a3d
print("Aegis imported from:", a3d.__file__)
# Low-level decoder APIs live under the `a3d` package.

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Outputs

• Metrics: out/metrics.csv, out/metrics.png
• Threshold: out/threshold.png
• Artifacts: out/gkp_lut.h

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Development

# 1) Clone
git clone https://github.com/hamidbahri92/Aegis.git
cd Aegis

# 2) Virtualenv
python -m venv .venv
# Linux/macOS:
source .venv/bin/activate
# Windows PowerShell:
.\.venv\Scripts\Activate

# 3) Editable install with extras
pip install -e ".[full]" --prefer-binary

# 4) Pre-commit
pre-commit install
pre-commit run --all-files

# 5) Tests
pytest -q

# 6) Sanity check (CLI)
aegis-run
aegis-metrics
aegis-threshold
aegis-export-header

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CI / Support Matrix

• OS: Windows, Ubuntu
• Python: 3.9, 3.12
• Linting/formatting: ruff, black via pre-commit
• Dependency updates: Dependabot

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Releasing

1. Bump version in pyproject.toml.
2. Commit via PR (CI must be green).
3. Tag the commit: git tag vX.Y.Z && git push origin vX.Y.Z.
4. GitHub Actions workflow Release (PyPI) builds and uploads to PyPI.

Required secret

Add a repository Actions secret:

• Name: PYPI_API_TOKEN
• Value: PyPI API token for the project (Repo → Settings → Secrets and variables → Actions → New repository secret)

For staging, the Release (TestPyPI) workflow targets TestPyPI.

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Project Layout

a3d/                       # Core package & decoders (MWPM, ...)
main.py                    # CLI: aegis-run
main_metrics.py            # CLI: aegis-metrics
main_threshold.py          # CLI: aegis-threshold
main_export_header.py      # CLI: aegis-export-header
.github/workflows/         # CI + TestPyPI/PyPI releases
pyproject.toml             # Packaging + entry points
README.md                  # This file
LICENSE                    # MIT

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License

MIT — see LICENSE.

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Links

• PyPI: https://pypi.org/project/aegis-qec/
• GitHub: https://github.com/hamidbahri92/Aegis
• Releases/Changelog: https://github.com/hamidbahri92/Aegis/releases

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### PowerShell: write README, commit, push

Run this from the repo root (`C:\Users\admin\Desktop\aegis`):

```powershell
$utf8NoBom = New-Object System.Text.UTF8Encoding $false
$readme = @'
# Aegis – Hardware-Aware Quantum Error Correction Toolkit

[![PyPI](https://img.shields.io/pypi/v/aegis-qec.svg)](https://pypi.org/project/aegis-qec/)
![Python](https://img.shields.io/pypi/pyversions/aegis-qec.svg)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
![CI](https://github.com/hamidbahri92/Aegis/actions/workflows/ci.yml/badge.svg)

Aegis is a modular toolkit for simulated quantum error correction with a focus on clarity, performance, and smooth automation from local dev to PyPI release.

## Features
- Decoders: Greedy (+ OSD fallback) and MWPM
- Reusable decoding graphs
- Metrics & threshold plots (CSV/PNG)
- CLI: aegis-run, aegis-metrics, aegis-threshold, aegis-export-header
- CI (Win/Ubuntu, Py 3.9/3.12), pre-commit (black+ruff), Dependabot
- Tagged releases to TestPyPI/PyPI via GitHub Actions
> Python 3.9+ required.

## Installation
```bash
pip install aegis-qec==1.0.6
pip install "aegis-qec[full]==1.0.6"

Quickstart (CLI)

aegis-run
aegis-metrics
aegis-threshold
aegis-export-header

Quickstart (Python)

import a3d
print("Aegis imported from:", a3d.__file__)

Outputs

• out/metrics.csv, out/metrics.png
• out/threshold.png
• out/gkp_lut.h

Development

git clone https://github.com/hamidbahri92/Aegis.git
cd Aegis
python -m venv .venv
.\.venv\Scripts\Activate    # on Windows
pip install -e ".[full]" --prefer-binary
pre-commit install
pre-commit run --all-files
pytest -q

Releasing

1. Bump version in pyproject.toml
2. Merge via PR (CI green)
3. Tag & push: git tag vX.Y.Z && git push origin vX.Y.Z
4. Release (PyPI) workflow publishes to PyPI

Secret required: PYPI_API_TOKEN (Repo → Settings → Secrets → Actions)

License

MIT

Links

• PyPI: https://pypi.org/project/aegis-qec/
• GitHub: https://github.com/hamidbahri92/Aegis
• Releases: https://github.com/hamidbahri92/Aegis/releases '@

[IO.File]::WriteAllText("$PWD\README.md", $readme, $utf8NoBom) git add README.md git commit -m "docs: English README (install, CLI, dev, release)" git push -u origin HEAD

New in this build

• Union‑Find‑with‑Erasures decoder (UF‑E): fast baseline with erasure‑aware edge ordering.
• LLR/NLL weighting for Greedy & MWPM via a shared stats module.

Transformer reweighter (optional, no hard dependency)

Set reweighter_type="transformer" in AegisConfig to enable an edge-cost reweighting pass using a compact Transformer. If PyTorch is unavailable, Aegis silently falls back to base costs.

New features

• UF‑E decoder with erasure peeling.
• Pipelined MWPM (two-pass with local correlation reweighting).
• Stim/DEM adapter for minimal detector-error-model demos.
• Transformer reweighter and tiny training script (optional Torch).
• One‑click CI via KITTY_RUN_CI.py and aegis-ci console script.
• Dockerfile for containerized runs.

Quick starts

• UF‑E: set decoder_type="uf".

• Pipelined MWPM: set decoder_type="mwpm2".

• Transformer reweighter: cfg.reweighter_type="transformer".

• Bench: python bench/run_threshold.py.

• Train transformer (optional Torch): python -m scripts.train_transformer.

Advanced features

• Correlation-aware MWPM (decoder_type="mwpm_corr") applying motif-based local correlation costs before MWPM.

• Fuller Stim/DEM adapter with shift_detectors time-slicing for demos.

• Educational Notebook: notebooks/educational_demo.ipynb.

• BP reweighter: cfg.reweighter_type="bp" for Torch-free, principled cost refinement.

Certificates, Confidence & Profiling

• Enable post-decode OSD polish with cfg.run_certificate=True (default) and cfg.certificate_mode="osd".
• Each DecodeResult now has a confidence field derived from avg cost (logit mapping).
• Set cfg.profile=True to log per-decode timing to logs/profile.csv.
• Batch API: DecoderRuntime.decode_batch_from_syndromes_uniform([...],[...]) for throughput benchmarks.
• Plots: python -m bench.generate_plots writes CSVs and PNGs (if matplotlib is available).
• DEM cross-validation: python -m scripts.dem_cross_validate path/to/model.dem (uses optional PyMatching/Stim).

GUI Dashboard

• Launch with aegis-gui (after pip install ".[gui]") or python -m scripts.run_gui.
• Adjust decoders/reweighters; run demo decodes and see quick sweep charts inline.

Soft Information (LLR) & Leakage

• Use a3d.llr_schema.SoftSyndrome and validate_soft().
• Decode via DecoderRuntime.decode_from_softinfo(llr_X, llr_Z, ...).
• Leakage adaptivity is applied via boundary-friendly pregrowth.

Unified benchmarking CLI

Use aegis-bench for all sweeps and plots. Legacy scripts delegate to it internally.

Speed path (optional)

• Set cfg.prefer_pymatching = True and use decode_from_dem_text(...) to leverage PyMatching+Stim when available.
• For general graphs, our internal decoders run; DEM interop path uses PyMatching's optimized kernels if present.

Acceleration

• Optional Numba jit is used for small hot kernels in UF‑E when installed (pip install numba).

DEM parity

• When stim and pymatching are installed, tests/test_dem_structural_parity_optional.py performs structural sanity checks.