Advancing Quantum Temperature Sensors for Ultra-Precise Measurements (UPMs): A Comparative Study
Bibliographic record
Abstract
In this study, we compared the performance of quantum temperature sensors (QTSs) with conventional sensors (CSs), highlighting differences in measurement accuracy and stability. Quantum sensors (QSs), known for their ability to provide ultra-precise measurements (UPMs), were tested across a temperature range of −10 to 40 °C. The results indicate that QSs offer superior accuracy, with a lower average error and a smaller standard deviation compared to CSs, indicating better measurement stability. For this comparison, we utilized Python scripts to conduct simulations and statistical analyses, leading to precise and reproducible results. The sensor performance was simulated in a controlled environment, and the obtained data were compared with experimental results. This comparison reveals that QSs are more reliable for applications requiring high precision, such as those in the Internet of Things (IoT) domain. These findings underscore the potential advantage of QSs in critical systems where measurement accuracy is paramount.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".