Quantum Brain-Computer Interface
Bibliographic record
Abstract
As the importance of quantum technologies is rapidly rising, e.g., in cyber security domain, further surge applications of such systems in various domains are expected in near term.In fact, the integration of quantum technologies with classical systems is inevitable in many disciplines, particularly as quantum devices are becoming more accessible.Among the various areas in which quantum technologies can potentially provide important complementary contributions, the area of Brain Computer Interface (BCI) has yet to be explored.The two areas of experimental photon quantum mechanics and BCI are brought together in this paper, for the first time.The applications of EEG signals to the control of mechanical systems, for example controlling drones remotely with brain wave signals, have been studied by various researchers.This paper presents the mechanism in which an EEG device is used to control the quantum properties in quantum experimental setups.In particular, a brain wave signal is used in changing the polarization of photons using a motorized half-waveplate in pre-built quantum entanglement and quantum cryptography experiments.This, in fact shows the mechanism on how quantum-BCI can be integrated as a system, before BCIbased control can be applied to hybrid classical-quantum systems, such as quantum experiments mounted on robotic platforms.The mechatronics of such systems is explained in this paper.This paper also outlines the necessary educational aspects of quantum related topics for students who have not traditionally exposed to quantum mechanics in their discipline, such as mechanical, aerospace, and electromechanical engineering students, which indeed is an essential step in preparing the next generation of engineering workforce for the fast-changing industry, the R&D sectors and academia.The quantum mechanics education and training steps in the mechatronics course and senior design projects are particularly promoted and discussed here.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".