mdopt: A code-agnostic tensor-network decoder for quantum error-correcting codes
Why this work is in the frame
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Bibliographic record
Abstract
mdopt is an open-source Python library for code-agnostic decoding of quantum error-correcting codes using tensor networks (in particular, MPS and MPO).Given only the parity-check matrices and a noise model, mdopt builds a tensor-network representation of the decoding problem in the form of MPS-MPO evolution and contracts it to approximate (or, for small instances, exact) maximum-likelihood posteriors over logical operators.Depending on the number of logical qubits, the code either reads out the posterior maximum directly or uses a DMRG-like variational algorithm to find the most likely logical correction.The library targets researchers who wish to benchmark codes and noise models beyond simple settings, while retaining a clear and reproducible workflow in pure Python.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it