feat(qec): surface-code noise model upscaling for training (6e-3 target)#2
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Replace the old 1e-3 "sparsity guard" in train.py with a proper noise-model upscaling module in noise_model.py that: - Scales all 25 noise-model parameters so max(grouped totals) = 6e-3 (just below surface-code threshold ~7.5e-3) for training data only. - Skips upscaling for non-surface-code types. - Provides skip_noise_upscaling config flag and PREDECODER_SKIP_NOISE_UPSCALING=1 env var to bypass upscaling. - Emits clear warnings when noise is above target or downscale is skipped. Adds 10 unit tests covering upscale, downscale, skip, non-surface-code, zero-totals, and reference-preservation scenarios. Updates README with detailed documentation of the upscaling rules and how to disable them. Ported from gitlab MR !33 (feature/surface-code-noise-upscale-6e3). Made-with: Cursor
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…et) (#2) * feat(qec): surface-code noise model upscaling for training (6e-3 target) Replace the old 1e-3 "sparsity guard" in train.py with a proper noise-model upscaling module in noise_model.py that: - Scales all 25 noise-model parameters so max(grouped totals) = 6e-3 (just below surface-code threshold ~7.5e-3) for training data only. - Skips upscaling for non-surface-code types. - Provides skip_noise_upscaling config flag and PREDECODER_SKIP_NOISE_UPSCALING=1 env var to bypass upscaling. - Emits clear warnings when noise is above target or downscale is skipped. Adds 10 unit tests covering upscale, downscale, skip, non-surface-code, zero-totals, and reference-preservation scenarios. Updates README with detailed documentation of the upscaling rules and how to disable them. Ported from gitlab MR !33 (feature/surface-code-noise-upscale-6e3). Made-with: Cursor * Apply yapf style to rebased branch Made-with: Cursor
ivanbasov
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…et) (#2) * feat(qec): surface-code noise model upscaling for training (6e-3 target) Replace the old 1e-3 "sparsity guard" in train.py with a proper noise-model upscaling module in noise_model.py that: - Scales all 25 noise-model parameters so max(grouped totals) = 6e-3 (just below surface-code threshold ~7.5e-3) for training data only. - Skips upscaling for non-surface-code types. - Provides skip_noise_upscaling config flag and PREDECODER_SKIP_NOISE_UPSCALING=1 env var to bypass upscaling. - Emits clear warnings when noise is above target or downscale is skipped. Adds 10 unit tests covering upscale, downscale, skip, non-surface-code, zero-totals, and reference-preservation scenarios. Updates README with detailed documentation of the upscaling rules and how to disable them. Ported from gitlab MR !33 (feature/surface-code-noise-upscale-6e3). Made-with: Cursor * Apply yapf style to rebased branch Made-with: Cursor
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Summary
train.pywith a proper noise-model upscaling module (get_training_upscaled_noise_modelinnoise_model.py) that scales all 25 noise-model parameters somax(grouped totals) = 6e-3(just below surface-code threshold ~7.5e-3) for training data only. Evaluation uses the user-specified noise model as-is.skip_noise_upscaling: truein config orPREDECODER_SKIP_NOISE_UPSCALING=1env var to bypass upscaling (useful for benchmarking/controlled experiments).Ported from internal GitLab MR !33 (
feature/surface-code-noise-upscale-6e3).Changed files
README.mdcode/qec/noise_model.pyget_grouped_totals,get_training_upscaled_noise_model, constantscode/tests/test_noise_model.pyTestNoiseModelUpscalingclass (10 tests)code/training/train.pyTest plan
PYTHONPATH=code python -m unittest discover -s code/tests -p "test_noise_model.py")Made with Cursor