Optimal and robust quantum state tomography of star-topology register
Why this work is in the frame
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Bibliographic record
Abstract
Abstract While quantum state tomography plays a vital role in the verification and benchmarking of quantum systems, it is an intractable task if the controllability of the quantum registers is constrained. In this paper, we propose a novel scheme for optimal and robust quantum state tomography for systems with constrained controllability. Based on the specific symmetry, we decompose the Hilbert space to alleviate the complexity of tomography and design a compact strategy with the minimum number of measurements. To switch between these measurement settings, we adopted parameterized quantum circuits consisting of local operations and free evolution, which are easy to implement in most practical systems. Then the parameters of these circuits were optimized to improve the robustness against errors of measurements. We demonstrated the experimental feasibility of our method on a 4-spin star-topology register and numerically studied its ability to characterize large-scale systems on a 10-spin star-topology register, respectively. Our results can help future investigations of quantum systems with constrained ability of quantum control and measurement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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