ILPQEC

ILPQEC is a Python package for maximum-likelihood quantum error correction decoding using integer linear programming (ILP). It turns parity-check matrices or Stim DetectorErrorModels into an ILP and solves it with a built-in backend out of the box. It is aimed at correctness-focused baselines, solver comparisons, and small-to-medium code studies rather than high-throughput production decoding.

Documentation: https://nzy1997.github.io/ILPQEC/

Scope and Highlights

What it does well:

• Out-of-the-box decoding with minimal setup.
• Inputs from parity-check matrices or Stim DetectorErrorModel.
• Maximum-likelihood decoding via weights or error probabilities.
• PyMatching-like API for easy experimentation.

When it is not a fit:

• Large code distances or high-shot workloads where ILP scaling dominates; use MWPM/BPOSD for throughput.

Installation

Python Environment Setup

ILPQEC supports Python 3.9+ (3.9–3.12 recommended). If you plan to use Gurobi, install Python < 3.13 because gurobipy wheels are not available on 3.13 at the moment.

Create and activate a virtual environment:

uv venv
source .venv/bin/activate
uv pip install --upgrade pip

On Windows:

uv venv
.venv\\Scripts\\activate
uv pip install --upgrade pip

Install the Package

PyPI package and import name: ilpqec.

# Basic installation
uv pip install ilpqec

# With Stim support
uv pip install ilpqec[stim]

# With sinter integration
uv pip install ilpqec[sinter]

# With SciPy sparse-matrix support
uv pip install ilpqec[scipy]

Development Setup

# Clone the repository
git clone https://github.com/nzy1997/ILPQEC
cd ILPQEC

# Create virtual environment (using uv)
uv venv
source .venv/bin/activate

# Install with dev dependencies
uv pip install -e ".[dev]"

Running Tests Locally

# Run all tests
uv run pytest tests/ -v

# Run specific test file
uv run pytest tests/test_decoder.py -v

# Run a quick functionality check
uv run python main.py

Running Examples

uv run python examples/basic_usage.py
uv run python examples/surface_code_example.py
uv run --python benchmark/.venv/bin/python benchmark/benchmark_decoders.py \
  --shots 10000 --distance 3 --rounds 3 --noise 0.01

Serve Docs Locally

# From the repo root
uv pip install -e ".[docs]"
uv run mkdocs serve

Quick Start

Parity-Check Matrix Decoding

import numpy as np
from ilpqec import Decoder

# Define a simple repetition code parity-check matrix
H = np.array([
    [1, 1, 0, 0, 0],
    [0, 1, 1, 0, 0],
    [0, 0, 1, 1, 0],
    [0, 0, 0, 1, 1],
])

# Create decoder
decoder = Decoder.from_parity_check_matrix(H)

# Decode a syndrome
syndrome = [1, 0, 0, 1]
correction = decoder.decode(syndrome)
print(f"Correction: {correction}")

Note: passing SciPy sparse matrices requires scipy to be installed (e.g., uv pip install ilpqec[scipy]).

Stim DetectorErrorModel Decoding

import stim
from ilpqec import Decoder

# Generate a surface code circuit
circuit = stim.Circuit.generated(
    "surface_code:rotated_memory_x",
    distance=3,
    rounds=3,
    after_clifford_depolarization=0.01
)

# Get detector error model
dem = circuit.detector_error_model(decompose_errors=True)

# Create decoder
decoder = Decoder.from_stim_dem(dem)

# Sample and decode
sampler = circuit.compile_detector_sampler()
detection_events, observables = sampler.sample(shots=100, separate_observables=True)

for i in range(10):
    _, predicted_obs = decoder.decode(detection_events[i])
    print(f"Shot {i}: predicted={predicted_obs}, actual={observables[i]}")

Stim DEM Support Notes

• Only error(p) lines are parsed; tags in error[...] are ignored. detector and logical_observable metadata lines are ignored. shift_detectors offsets are applied. repeat blocks are flattened by default; this can expand large DEMs. detector_separator is unsupported and raises an error.
• The ^ separator is treated as whitespace and does not change parsing.
• If you want to fail fast instead of flattening, pass flatten_dem=False.

Sinter Integration (optional)

ILPQEC includes a sinter decoder wrapper for benchmarking and sampling. Install with:

uv pip install ilpqec[sinter]

Example usage:

import sinter
import stim
from ilpqec.sinter_decoder import SinterIlpDecoder

circuit = stim.Circuit.generated(
    "surface_code:rotated_memory_x",
    distance=3,
    rounds=3,
    after_clifford_depolarization=0.01,
)

tasks = [
    sinter.Task(
        circuit=circuit,
        decoder="ilpqec",
    )
]

stats = sinter.collect(
    tasks=tasks,
    custom_decoders={"ilpqec": SinterIlpDecoder()},
)

Notes:

• The sinter adapter currently uses the direct HiGHS backend only.
• ilpqec[sinter] includes stim and sinter dependencies.

Maximum-Likelihood Decoding with Weights

import numpy as np
from ilpqec import Decoder

H = np.array([[1, 1, 0], [0, 1, 1]])
error_probs = [0.1, 0.01, 0.1]

# Weights are computed automatically from probabilities
decoder = Decoder.from_parity_check_matrix(H, error_probabilities=error_probs)

syndrome = [1, 1]
correction, weight = decoder.decode(syndrome, return_weight=True)
print(f"ML correction: {correction}, weight: {weight}")

Note: error_probabilities must be in (0, 0.5]; pass explicit weights for p > 0.5.

Benchmark

Benchmarks use extra dependencies and optional solver backends. Use a dedicated virtual environment under benchmark/:

uv venv benchmark/.venv
uv pip install --python benchmark/.venv/bin/python --upgrade pip

# ILPQEC + optional solver backends
uv pip install --python benchmark/.venv/bin/python -e ".[pyomo,gurobi]"

# Benchmark dependencies
uv pip install --python benchmark/.venv/bin/python stim pymatching ldpc tesseract-decoder

Notes:

• Gurobi requires a valid license and Python < 3.13.
• If you do not need Gurobi, drop gurobi from the extras.
• BPOSD runs with max_iter=50, osd_order=0, and bp_method=minimum_sum.
• Tesseract runs with det_beam=50 by default (adjustable via --tesseract-beam).

Circuit-level rotated surface code memory

uv run --python benchmark/.venv/bin/python benchmark/benchmark_decoders.py \
  --compare-ilp-solvers --ilp-solvers highs,scip,gurobi,cbc,glpk \
  --shots 10000 --distance 3 --rounds 3 --noise 0.01

Results from a local macOS arm64 run (shots=10000, your numbers will vary):

Decoder	Time (ms/shot)	Logical Error Rate
ILP[highs] (direct)	2.7514	1.640%
ILP[gurobi] (direct)	0.6403	1.650%
ILP[scip]	28.2160	1.670%
ILP[cbc]	14.9315	1.670%
ILP[glpk]	8.6292	1.670%
MWPM (pymatching)	0.0035	2.150%
BPOSD (ldpc)	0.0308	7.680%
Tesseract	0.1602	1.640%

Code-capacity surface code (data errors only, perfect syndrome)

uv run --python benchmark/.venv/bin/python benchmark/benchmark_decoders.py \
  --noise-model code_capacity --compare-ilp-solvers --ilp-solvers highs,scip,gurobi,cbc,glpk \
  --shots 10000 --distance 3 --rounds 1 --noise 0.01

Results from a local macOS arm64 run (shots=10000, your numbers will vary):

Decoder	Time (ms/shot)	Logical Error Rate
ILP[highs] (direct)	3.2321	0.070%
ILP[gurobi] (direct)	0.0838	0.070%
ILP[scip]	23.4834	0.070%
ILP[cbc]	10.4697	0.070%
ILP[glpk]	5.0085	0.070%
MWPM (pymatching)	0.0036	0.070%
BPOSD (ldpc)	0.0028	0.070%
Tesseract	0.0093	0.070%

Color code (color_code:memory_xyz)

uv run --python benchmark/.venv/bin/python benchmark/benchmark_decoders.py \
  --code-task color_code:memory_xyz --compare-ilp-solvers --ilp-solvers highs,scip,gurobi,cbc,glpk \
  --shots 10000 --distance 3 --rounds 3 --noise 0.01

Results from a local macOS arm64 run (shots=10000, your numbers will vary):

Decoder	Time (ms/shot)	Logical Error Rate
ILP[highs] (direct)	2.0226	4.450%
ILP[gurobi] (direct)	0.3184	4.420%
ILP[scip]	24.9402	4.420%
ILP[cbc]	11.6961	4.450%
ILP[glpk]	6.0799	4.420%
MWPM (pymatching)	0.0034	13.420%
BPOSD (ldpc)	0.0114	9.830%
Tesseract	0.0600	4.450%

API Reference

Decoder

Main decoder class.

Class Methods:

• from_parity_check_matrix(H, weights=None, error_probabilities=None, solver=None) - Create from parity-check matrix
• from_stim_dem(dem, solver=None, merge_parallel_edges=True, flatten_dem=True) - Create from Stim DetectorErrorModel

Instance Methods:

• decode(syndrome, return_weight=False) - Decode a single syndrome
• decode_batch(syndromes) - Decode multiple syndromes
• set_solver(name, **options) - Switch solver

Properties:

• num_detectors - Number of parity checks/detectors
• num_errors - Number of error mechanisms
• num_observables - Number of logical observables (for DEM)
• solver_name - Current solver name

get_available_solvers()

Returns a list of available solver names.

from ilpqec import get_available_solvers
print(get_available_solvers())  # e.g., ['scip', 'highs', 'cbc']

License

MIT License