Bibliographic record
Abstract
The Internet is agog with stories about quantum radar. Will it really make stealth aircraft vulnerable? Were the microwave experiments reported in 2020 from Europe and Canada really proof-of-principle laboratory demonstrations of quantum radar's advantage over classical radar? Or, does the 25 September 2020 news article in Science-entitled "The short, strange life of quantum radar"-paint the true picture? This paper disentangles microwave quantum radar's alphabet soup: quantum illumination (QI) radar, quantum illumination with a microwave parametric amplifier receiver (QI-MPA) radar, quantum-correlated noise (QCN) radar, and quantum-correlated noise radar with a correlation receiver (QCN-CR). In particular, it evaluates-with no explicit quantum-mechanical notation or calculations-these radars' miss probabilities at fixed false-alarm probability and it compares them to those for classical radar's relevant alphabet soup, viz., coherent-state homodyne (CS-Hom) radar, coherent-state heterodyne (CS-Het) radar, classically-correlated noise (CCN) radar, and classically-correlated noise radar with a correlation receiver (CCN-CR). These comparisons show that, under ideal operating conditions, the QI and QI-MPA radars offer performance advantages over their best classical counterparts. Moreover, QI-MPA's advantage is similar to that for its error-probability exponent when target absence and presence are equally likely and all radars make minimum error-probability decisions based on their respective measurements. Available theory, however, is unable to fully quantify QI's advantage in the operating regime of interest. Ultimately-after accounting for problems that afflict QI and QI-MPA, but not their classical competitors, and factoring in realistic standoff-sensing parameters-it will be concluded that the aforementioned Science article has it correct. QI target detection has little to offer for standoff sensing, i.e., it does not compromise stealth aircraft.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".