Release/v1.2.0 rc.1

README.md

@@ -240,6 +240,15 @@ Xanadu-style job conversion example:
 bash examples/hardware_integration/run.sh
 ```
 
+Aurora/QCA/GKP fixture conversions:
+
+```bash
+bash examples/hardware_integration/run_public_datasets.sh
+```
+
+For large real datasets, use converter streaming/chunk flags:
+`--stream --shot-start <N> --max-shots <K> [--append-out]`.
+
 Replay converted NDJSON through the C++ adapter:
 
 ```bash

docs/hardware-integration.md

@@ -29,13 +29,26 @@ Key fields:
 Each line is one `DecodeRequest` JSON object.
 See `schemas/decoder_io_example.ndjson` for examples.
 
-### Xanadu job conversion helper
+### Xanadu dataset conversion helper
 
-Use the built-in converter example to transform Xanadu-style job outputs into
-`DecodeRequest` NDJSON:
+Use the built-in converter example to transform Xanadu data into
+`DecodeRequest` NDJSON.
+
+Supported source modes:
+
+- `xanadu_job_json`: legacy job payloads with `output`/`samples`.
+- `aurora_switch_dir`: Aurora decoder-demo batch directory with
+  `switch_settings_qpu_*.npy` (or `.json` in fixture mode).
+- `shot_matrix`: generic shot arrays from `.json`, `.npy`, or `.npz`
+  (covers QCA `samples.npy`).
+- `count_table_json`: count-compressed outcomes (`sample` + `count`), useful for
+  GKP-style exports.
+
+Legacy job JSON:
 
 ```bash
 python3 examples/hardware_integration/convert_xanadu_job_to_decoder_io.py \
+  --source-format xanadu_job_json \
   --input /path/to/xanadu_job.json \
   --mapping /path/to/your_mapping.json \
   --out examples/results/hardware_integration/decoder_requests.ndjson
@@ -47,9 +60,85 @@ Quick demo:
 bash examples/hardware_integration/run.sh
 ```
 
+Aurora / QCA / GKP fixture demos:
+
+```bash
+bash examples/hardware_integration/run_public_datasets.sh
+```
+
+Real Aurora decoder-demo batch:
+
+```bash
+python3 -m pip install numpy
+
+python3 examples/hardware_integration/convert_xanadu_job_to_decoder_io.py \
+  --source-format aurora_switch_dir \
+  --stream \
+  --input /path/to/decoder_demo/signal/batch_0 \
+  --mapping examples/hardware_integration/xanadu_aurora_mapping_example.json \
+  --out examples/results/hardware_integration/decoder_requests_aurora.ndjson \
+  --aurora-binarize \
+  --max-shots 20000 \
+  --progress-every 5000
+```
+
+Real QCA sample matrix:
+
+```bash
+python3 examples/hardware_integration/convert_xanadu_job_to_decoder_io.py \
+  --source-format shot_matrix \
+  --stream \
+  --input /path/to/fig3a/samples.npy \
+  --mapping examples/hardware_integration/xanadu_qca_mapping_example.json \
+  --out examples/results/hardware_integration/decoder_requests_qca.ndjson \
+  --max-shots 50000 \
+  --progress-every 10000
+```
+
+Chunk large QCA files by repeating conversion with shifted `--shot-start` and `--append-out`:
+
+```bash
+python3 examples/hardware_integration/convert_xanadu_job_to_decoder_io.py \
+  --source-format shot_matrix \
+  --stream \
+  --input /path/to/fig3a/samples.npy \
+  --mapping examples/hardware_integration/xanadu_qca_mapping_example.json \
+  --out examples/results/hardware_integration/decoder_requests_qca.ndjson \
+  --shot-start 0 \
+  --max-shots 200000
+
+python3 examples/hardware_integration/convert_xanadu_job_to_decoder_io.py \
+  --source-format shot_matrix \
+  --stream \
+  --input /path/to/fig3a/samples.npy \
+  --mapping examples/hardware_integration/xanadu_qca_mapping_example.json \
+  --out examples/results/hardware_integration/decoder_requests_qca.ndjson \
+  --append-out \
+  --shot-start 200000 \
+  --max-shots 200000
+```
+
+Count-compressed GKP outcomes:
+
+```bash
+python3 examples/hardware_integration/convert_xanadu_job_to_decoder_io.py \
+  --source-format count_table_json \
+  --input /path/to/gkp_outcome_counts.json \
+  --mapping examples/hardware_integration/xanadu_gkp_mapping_example.json \
+  --out examples/results/hardware_integration/decoder_requests_gkp.ndjson
+```
+
 The mapping file controls how measured modes are converted to syndrome events.
 See `examples/hardware_integration/xanadu_syndrome_mapping_example.json`.
 
+Large-data controls:
+
+- `--stream`: use memory-mapped loading where possible (notably `.npy`).
+- `--shot-start`: skip the first `N` expanded shots.
+- `--max-shots`: cap this run to `K` shots.
+- `--append-out`: append to an existing NDJSON file.
+- `--progress-every`: print progress every `M` written requests.
+
 ### Replay NDJSON through LiDMaS+ adapter
 
 Use the C++ CLI replay mode to decode each NDJSON `DecodeRequest` line and write

docs/releases/v1.2.0-rc.1.md

@@ -0,0 +1,58 @@
+# 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 examples/hardware_integration/convert_xanadu_job_to_decoder_io.py`
+- Fixture conversions:
+  - `bash examples/hardware_integration/run.sh`
+  - `bash examples/hardware_integration/run_public_datasets.sh`
+- Replay checks:
+  - `bash examples/hardware_integration/replay.sh examples/results/hardware_integration/decoder_requests_aurora.ndjson`
+  - `bash examples/hardware_integration/replay.sh examples/results/hardware_integration/decoder_requests_qca.ndjson`
+  - `bash examples/hardware_integration/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]`

docs/releases/v1.2.0-rc.2.md

@@ -0,0 +1,58 @@
+# LiDMaS+ v1.2.0-rc.2
+
+Release date: 2026-03-17
+
+Tag: `v1.2.0-rc.2`  
+Package version: `1.2.0rc2`
+
+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.2`
+
+## Validation Performed
+
+- Python compile check:
+  - `python3 -m py_compile examples/hardware_integration/convert_xanadu_job_to_decoder_io.py`
+- Fixture conversions:
+  - `bash examples/hardware_integration/run.sh`
+  - `bash examples/hardware_integration/run_public_datasets.sh`
+- Replay checks:
+  - `bash examples/hardware_integration/replay.sh examples/results/hardware_integration/decoder_requests_aurora.ndjson`
+  - `bash examples/hardware_integration/replay.sh examples/results/hardware_integration/decoder_requests_qca.ndjson`
+  - `bash examples/hardware_integration/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.2`
+- For large QCA/Aurora inputs, prefer streaming/chunking:
+  - `--stream --shot-start <N> --max-shots <K> [--append-out]`

examples/README.md

@@ -27,7 +27,7 @@ LIDMAS_SKIP_PY_DEPS=1 ./examples/hybrid_threshold/run.sh
 - `decoder_comparison/`: same sweep across multiple decoders.
 - `failure_debug/`: stress run and failure-dump capture workflow.
 - `plot_only/`: publication-grade plotting from existing CSV files.
-- `hardware_integration/`: convert Xanadu-style job output JSON to LiDMaS+ decoder IO NDJSON.
+- `hardware_integration/`: convert Xanadu datasets (Aurora/QCA/GKP/job JSON) to LiDMaS+ decoder IO NDJSON.
 
 
 ## Central Results Folder