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Update README.md to have DISTANCE and N_ROUNDS information #44

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ivanbasov merged 3 commits into from
Apr 7, 2026
Merged

Update README.md to have DISTANCE and N_ROUNDS information #44

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DISTANCE and N_ROUNDS updates
bmhowe23 Apr 6, 2026
757f378
Formatting updates
bmhowe23 Apr 6, 2026
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Revert "Formatting updates"
bmhowe23 Apr 6, 2026
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23 changes: 20 additions & 3 deletions README.md
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Expand Up @@ -138,8 +138,16 @@ The pre-trained public models use `--model-id 1` (R=9) and `--model-id 4` (R=13)
After training (or starting from the shipped `.safetensors` files), you can export the model to
ONNX and optionally apply INT8 or FP8 post-training quantization for deployment.

Set the `ONNX_WORKFLOW` and (optionally) `QUANT_FORMAT` environment variables before running
inference with `local_run.sh`:
You may also change the surface code distance and number of rounds at inference
time. That is - you are not required retrain a new model when changing either
one of these parameters; since the model is a 3D convolutional neural network,
the model will simply be run over a new decoding volume.

- To run with a new distance, simply add `DISTANCE=<your distance>` to the commands below.
- To run with a new number of rounds, simply add `N_ROUNDS=<your number of rounds>` to the commands below.

Set the `ONNX_WORKFLOW` and (optionally) (`QUANT_FORMAT`, `DISTANCE`,
`N_ROUNDS`) environment variables before running inference with `local_run.sh`:

| `ONNX_WORKFLOW` | Behavior |
|---|---|
Expand Down Expand Up @@ -169,7 +177,16 @@ ONNX_WORKFLOW=3 WORKFLOW=inference bash code/scripts/local_run.sh
| `QUANT_FORMAT` | unset | `int8` or `fp8`. Unset means no quantization (FP32 ONNX). |
| `QUANT_CALIB_SAMPLES` | `256` | Calibration samples for INT8/FP8 post-training quantization. |

**Circuit variables:**

| Variable | Default | Description |
|---|---|---|
| `CONFIG_NAME` | `config_public` | Use the defaults from the `conf/$CONFIG_NAME.yaml` file |
| `DISTANCE` | Use the distance specified in the `conf/$CONFIG_NAME.yaml` file | surface code distance |
| `N_ROUNDS` | Calibration samples for INT8/FP8 post-training quantization. | number of rounds in memory experiment |

Notes:

- TensorRT workflows (`ONNX_WORKFLOW=2` or `3`) require `tensorrt` and `modelopt`.
- FP8 quantization failure is fatal. INT8 failure falls back to the FP32 ONNX model silently.
- ONNX and engine files are written to the current working directory.
Expand Down Expand Up @@ -215,7 +232,7 @@ Results are written to `outputs/<EXPERIMENT_NAME>/plots/`.
| Decoder | Source | Notes |
|---|---|---|
| No-op | — | Pre-decoder output only, no global correction |
| Union-Find | `ldpc` | Fast, sub-optimal |
| Union-Find | `ldpc` | Fast, sub-optimal LER (Logical Error Rate) |
| BP-only | `ldpc` | Belief propagation, no OSD |
| BP+LSD-0 | `ldpc` | BP with localized statistics decoding |
| Uncorr-PM | PyMatching | Uncorrelated minimum-weight perfect matching |
Expand Down
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