Impact of emerging quantum information technologies (QIT) on information fusion: panel summary (Conference Presentation)
Bibliographic record
Abstract
Quantum physics has a growing influence on sensor technology; particularly, in the areas of quantum computer science, quantum communications, and quantum sensing based on recent insights from atomic, molecular and optical physics. These quantum contributions have the potential to impact information fusion techniques. Quantum information technology (QIT) methods of interest suggest benefits for information fusion, so a panel was organized to articulate methods of importance for the community. The panel discussion presented many ideas from which the leading impact for information fusion is directly related to the sub-Rayleigh sensing that reduces uncertainty for object assessment through enhanced resolution. The second areas of importance is in the cyber security of data that supports data, sensor, and information fusion. Some elements of QIT that require further analysis is in quantum computing for which only a limited set of information fusion techniques can harness the methods associated with quantum computer architectures. The panel reviewed various aspects of QIT for information fusion which provides a foundation to identify future alignment between quantum and information fusion techniques.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".