Quantum Encryption in Phase Space for Coherent Optical Communications
Bibliographic record
Abstract
Optical layer attacks on communication networks are one of the weakest reinforced areas of the network, allowing attackers to overcome security when proper safeguards are not put into place. Here, we present our solution or Quantum Encryption in Phase Space (QEPS), a physical layer encryption method to secure data over the optical fiber, based on our novel round-trip Coherent-based Two-Field Quantum Key Distribution (CTF-QKD) scheme. We perform a theoretical study through simulation and provide an experimental demonstration. The same encryption is used for QEPS as CTF-QKD but achieved through a pre-shared key and one-directional transmission design. QEPS is uniquely different from traditional technology where encryption is performed at the optical domain with coherent states by applying a quantum phase-shifting operator. The pre-shared secret is used to seed a deterministic random number generator and control the phase modulator at the transmitter for encryption and at the receiver for decryption. Using commercially available simulation software, we study two preventative measures for different modulation formats which will prevent an eavesdropper from obtaining any data. QEPS demonstrates that it is secure against tapping attacks when attackers have no information of the phase modulator and pre-shared key. Finally, an experiment with commercial components demonstrates QEPS system integrability.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".