LiDMaS+ logo

LiDMaS+ is an open-source CLI toolkit for reproducible quantum error-correction simulation, decoder benchmarking, and hardware-to-decoder replay. This exists to make QEC experiments reproducible, scriptable, and directly comparable across codes, decoders, and hardware data pipelines.

Current coverage:

  • Correction code engines: Surface, CSS family (including custom CSS specs/matrices, repetition, and Shor), and LDPC.
  • GKP support: Available in current CLI flows as hybrid/native Surface workflows (--mode=hybrid and --mode=gkp), i.e., CV/GKP behavior integrated into Surface-mode experiments.
  • Decoders: mwpm, uf, bp, neural_mwpm, and stub.
  • Targeted hardware providers: IBM Quantum (live superconducting telemetry), Rigetti/Ankaa workflows (replay), and Xanadu datasets (Aurora/QCA/GKP replay).
  • Quantum software stacks: Qiskit IBM Runtime, PennyLane, Qiskit, Cirq, and planned Qibo/Qibolab integration.

It provides:

  • a unified CLI for running simulation and replay workflows,
  • deterministic runs with explicit seed control,
  • reusable examples for thresholds, replay, and analysis outputs.

If you need the full technical depth, use the published docs

Statement of Need

Quantum error-correction studies are often hard to reproduce across teams because workflows, decoder settings, and data formats vary across scripts and hardware sources.

LiDMaS+ addresses this by giving researchers and engineers a single CLI and repeatable workflow surface for:

  • deterministic simulation runs,
  • consistent decoder comparison,
  • hardware-to-decoder replay and artifact generation.

Model-Exact Scope

Let a run scope be

S ∈ 𝒮, S = (C, D, M, Θ, σ, I, V)

where:

  • C: code family/configuration,
  • D: decoder set,
  • M: execution mode,
  • Θ: algorithm/hyperparameter settings,
  • σ: seed and stochastic controls,
  • I: input stream or dataset identity,
  • V: executable/version identity.

Define the run key as:

K(S) = H(ser(S))

for a canonical serializer ser and collision-resistant hash H. LiDMaS stores K(S) with each result artifact.

Proposition: ∀ S₁,S₂ ∈ 𝒮, S₁ ≠ S₂ ⇒ Pr[K(S₁) ≠ K(S₂)] ≥ 1 − ε for negligible ε.

So, except with negligible probability, artifacts from S₁ and S₂ are scope-distinct.

Proof sketch:

  1. S₁ ≠ S₂ ⇒ ser(S₁) ≠ ser(S₂) (canonical serialization is injective on scope tuples).
  2. ∀ x ≠ y, Pr[H(x)=H(y)] ≤ ε by collision resistance.
  3. Substitute x=ser(S₁), y=ser(S₂): Pr[K(S₁)=K(S₂)] ≤ ε, hence Pr[K(S₁)≠K(S₂)] ≥ 1−ε.

Design-to-Result Workflow

Let experiment design be E = (C, D, 𝒩, T, σ).

Define scoped execution and outputs as: S = (E, M, Θ, I, V), K = H(ser(S)), R = Φ(S), A = (K, R, μ).

Pipeline: E →[encode in CLI] S →[Φ (simulate/replay)] R →[persist with K] A →[analyze] Δ →[rerun with S] R′ →[‖R − R′‖ ≤ τ] validated results

Step map:

  1. Specify E.
  2. Encode S in lidmas ... arguments.
  3. Execute Φ in the selected mode.
  4. Persist A=(K,R,μ).
  5. Compute comparison/analysis outputs Δ.
  6. Re-run to get R′ and check ‖R − R′‖ ≤ τ.
  7. Promote validated artifacts to reports/plots/paper bundles.

LiDMaS+ UI preview (active development)

UI status: under active development. For stable workflows today, use the CLI (lidmas) below.

Getting Started

Prerequisites

  • C++20 compiler
  • CMake >= 3.16
  • Python 3.9+ (for PyPI install path and optional scripts)
  • Optional: OpenMP
  • Optional: CUDA toolkit (GPU sampling path)

Installation

Install from PyPI:

python -m pip install --upgrade lidmas

This installs the lidmas CLI so you can run LiDMaS+ commands directly from your shell.

Or build from source:

cmake -S . -B build
cmake --build build -j

Usage

Show available commands:

lidmas --help

Run a quick smoke check:

lidmas --smoke

Run from source build (without PyPI install):

./build/lidmas --help
./build/lidmas --smoke

For full examples and workflow guides:

Hardware Integrations

Mode Integration Company / Provider Quantum Software Stack
Live IBM superconducting stream polling IBM Quantum Qiskit IBM Runtime
Live (planned) Qibolab hardware backend integration Qibo/Qibolab self-hosted labs Qibo + Qibolab
Replay Ankaa superconducting replay stream Rigetti (Ankaa workflows) LiDMaS adapter stream (fixture/HDF5 replay)
Replay Xanadu Aurora/QCA/GKP dataset conversion + replay Xanadu Python converter + LiDMaS decoder_io_replay
Replay Simulator framework replay PennyLane / Qiskit / Cirq ecosystems PennyLane, Qiskit, Cirq

Hardware Integration examples and commands are documented here

Contributing

Bug reports, feature requests, and pull requests are welcome.

Citation

If you use LiDMaS+ in academic work, cite the software release used for your experiments (tag + commit hash).

Paper reference (paper_03):

paper_03 graphic

@misc{wayo2026unifiedhardwaretodecoderarchitecturehybrid,
  title={A Unified Hardware-to-Decoder Architecture for Hybrid Continuous-Variable and Discrete-Variable Quantum Error Correction in LiDMaS+},
  author={Dennis Delali Kwesi Wayo and Chinonso Onah and Leonardo Goliatt and Sven Groppe},
  year={2026},
  eprint={2604.15389},
  archivePrefix={arXiv},
  primaryClass={quant-ph},
  url={https://arxiv.org/abs/2604.15389}
}

License

This project is licensed under the MIT License.
See LICENSE.

Project links

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Avatar for DennisWayo from gravatar.com DennisWayo

Credits

Author: Dennis Delali Kwesi Wayo

GitHub Statistics

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License

MIT License (MIT License)

Requires

Python >=3.9

Tags

quantum qec surface-code decoder simulation

Classifiers

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