docs(qec): tensor network noise-learning decoder docs#554
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Adds Sphinx API narrative and decoder docs for noise-learning integration, split from the integration PR for focused review. Signed-off-by: vedika-saravanan <vsaravanan@nvidia.com>
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…526) ## Summary Productizes Nico's `NMOptimizer` into the TN decoder. Fits per-error noise probabilities by backpropagating through a torch-backed tensor-network contraction. > `oe_torch` / `oe_torch_compiled` contractors aren't included, unused by `NMOptimizer`. ## What's in it - `NMOptimizer` + `make_compiled_step`, exposed top-level via `from cudaq_qec import NMOptimizer` - Three execute modes (`codegen` / `unrolled` / `opt_einsum`); optional `torch.compile`; prior auto-clamping - Unit tests parameterised over `(cpu, cuda)` × execute modes, plus `test_forward_parity_with_tn_decoder` against the base TN decoder - Example (`tn_noise_learning.py`) runs in CI as the end-to-end LER gate ## Test plan - [x] `pytest libs/qec/python/tests/test_nm_optimizer.py -v` - [x] `pytest libs/qec/python/tests/test_tensor_network_decoder.py -v` - [x] `python3 docs/sphinx/examples/qec/python/tn_noise_learning.py` ## Follow-ups - cuTensorNet backend (currently forced to torch for autograd), unlocks larger code distances (d=5+ surface codes) - KPI on real partner noise data - Decoder-agnostic mixin (QLDPC) - Docs: [#554](#554) NVBug for new deps: https://nvbugspro.nvidia.com/bug/6177164 --------- Signed-off-by: vedika-saravanan <vsaravanan@nvidia.com>
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main(or validate locally against that branch).:py:class:, etc.) resolve for noise-learning decoder APIs.