LiDMaS+ v1.2.0-rc.1

Release date: 2026-03-17

Tag: v1.2.0-rc.1
Package version: 1.2.0rc1

This release candidate focuses on Xanadu hardware-data integration and large-dataset conversion reliability.

Highlights

  • Added multi-format Xanadu data conversion into decoder_io NDJSON:
  • legacy job JSON (output/samples)
  • Aurora decoder-demo switch-setting batches
  • QCA/Borealis shot matrices (.json, .npy, .npz)
  • count-compressed outcome tables (GKP-style exports)
  • Added C++ replay workflow integration for converted NDJSON requests.
  • Added streaming/chunked conversion controls for heavy datasets:
  • --stream
  • --shot-start
  • --max-shots
  • --append-out
  • --progress-every
  • Added runnable fixtures/scripts for Aurora/QCA/GKP conversion and replay validation.

Included Changes Since v1.1.4

  • feat(hardware): add decoder_io NDJSON replay CLI and Xanadu integration example (431b0c9)
  • feat(hardware): add streaming Xanadu dataset integration (a544e2b)

Comparison:
https://github.com/DennisWayo/lidmas_cpp/compare/v1.1.4...v1.2.0-rc.1

Validation Performed

  • Python compile check:
  • python3 -m py_compile hardware_integration/xanadu/convert_xanadu_job_to_decoder_io.py
  • Fixture conversions:
  • bash hardware_integration/xanadu/run.sh
  • bash hardware_integration/xanadu/run_public_datasets.sh
  • Replay checks:
  • bash hardware_integration/xanadu/replay.sh examples/results/hardware_integration/decoder_requests_aurora.ndjson
  • bash hardware_integration/xanadu/replay.sh examples/results/hardware_integration/decoder_requests_qca.ndjson
  • bash hardware_integration/xanadu/replay.sh examples/results/hardware_integration/decoder_requests_gkp.ndjson
  • Result: replay completed with errors=0 on fixture datasets.

Notes

  • .npy/.npz conversion paths require NumPy (pip install numpy).
  • This is an RC intended for real-data validation at scale before final v1.2.0.
  • No breaking changes are expected for existing workflows.

Upgrade / Usage

  • Use this RC tag for validation runs:
  • git checkout v1.2.0-rc.1
  • For large QCA/Aurora inputs, prefer streaming/chunking:
  • --stream --shot-start <N> --max-shots <K> [--append-out]