Controlled Quantum Semantic Communication for Industrial CPS Networks
Bibliographic record
Abstract
Computing-intensive semantic communication emphasizes context, enabling the extraction of task-specific semantics from the source data and the reconstruction of the intended meaning at the destination. In industrial cyber-physical systems (CPSs), this approach can optimize automation processes while minimizing communication overhead with efficient bandwidth use in environments where machines, sensors, and controllers must communicate frequently. By integrating quantum communication with computing-empowered semantic methods, we can achieve unprecedented efficiency and security in task-oriented data transmission, effectively safeguarding against eavesdropping and other attacks. This paper presents a controlled quantum semantic communication (QSC) framework that leverages semantic extraction for anomaly detection in industrial CPS networks and employs controlled quantum communication to send the data securely with high semantic fidelity. A machine learning model extracts semantic information from images as the hull point data representing defective regions as pixel points. This data is then transmitted with high fidelity using quantum communication with controlled quantum state preparation. We use discrete- and continuous-variable states to simulate quantum binary phaseshift keying (BPSK) and$M$-ary pulse position modulation ($M$-PPM), respectively. At the receiver, these quantum states are measured using optimal quantum decision-making and converted back into the hull point data, thereby generating the anomaly map. This map is overlaid on a template image to highlight defect positions, which can be used for industrial quality control. Furthermore, we simulate the controlled QSC framework (BPSK and$M$-PPM) across a diverse set of anomaly detection examples and evaluate the QSC performance in industrial CPS networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".