Practical Advantage in Microwave Quantum Illumination
Bibliographic record
Abstract
Broadly speaking, in quantum illumination we can say that a proposed protocol has a “quantum advantage” if it outperforms all possible classical protocols. In the optical domain of LIDAR, this is the most useful metric as lasers can routinely produce nearly ideal classical states of light at room temperature (RT). This is not the case in the microwave domain of RADAR where the photon energy is much less than the 300K thermal energy, meaning that a real RT microwave source will always be contaminated by significant thermal noise. Thus, it is not clear if it is technologically possible to produce an ideal classical microwave signal at RT. It is therefore interesting to ask if a microwave quantum illumination protocol can have a “practical advantage” compared to the best technologically feasible RT microwave source. In this paper, we look to frame this question more precisely. As a concrete example, we present experimental results showing that, contrary to recent claims in the literature [1], an entangled microwave source amplified by a cryogenic HEMT amplifier fails to obtain any performance advantage over a simply constructed RT source and, in facts, performs significantly worse. We present a simple theory which explains the experimental results and which offers guidance on how a practical advantage might be achieved.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".