Rate-Limited Optimal Transport for Quantum Gaussian Observables
Bibliographic record
Abstract
The rate-limited optimal transport problem is introduced for the continuous-variable quantum measurement systems in the form of output-constrained rate-distortion coding. The main coding theorem provides a single-letter characterization of the achievable rate region for lossy quantum-to-classical source coding that transforms a sufficiently large tensor product of IID continuous-variable quantum states from a quantum source to a sequence of IID samples from a classical continuous destination distribution with a prescribed distortion level. The evaluation of rate region is performed for the systems with quantum Gaussian source and Gaussian destination distribution. We establish a Gaussian observable optimality theorem for such systems and provide an analytical formulation of the rate-limited quantum-classical Wasserstein distance in the case of isotropic and one-mode Gaussian quantum systems.11The proofs of the theorems and the details of the results are provided in the extended online version [1] available at https://arxiv.org/abs/2305.10004 for further reference. This work was supported in part by NSF grants CCF 2007878 and CCF 2132815.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".