PRL3 – Quantum Passive Observation
Autonomous rule extraction from quantum processor emissions – validated on real IBM hardware.
How it works
The system passively observes physical emissions (photons, phonons, etc.) from quantum processors without interfering with computation. Using hierarchical scopes (C1–C4) and a predictive reconfiguration layer (PRL), it learns operational patterns and autonomously detects repeatable rules – effectively “remembering” quantum behaviour. The architecture is hardware‑agnostic and can be applied to any quantum processor type.
Key components:
- Hierarchical filter scopes (C1–C4) – patterns float between scopes based on dynamic weight.
- PRL (Predictive Reconfiguration Layer) – computes anomaly scores (A‑score) from emission streams.
- Multi‑level alarms – L3 (trend), L2 (accelerated), L1 (immediate system‑wide update).
- Meta‑learning – after first rule detection, thresholds auto‑calibrate for faster future learning.
Validation on IBM Quantum (ibm_fez)
500 Bell state circuits submitted to the IBM cloud quantum processor. 218 jobs completed before instance usage limit. The system autonomously analysed measurement results and detected the first rule after only 5 completed jobs.
The discovered rule: Bell state outputs are almost always 00 or 11 with a ratio of 1.059 and 5.5% noise. Once learned, the system can answer subsequent identical queries from memory, cutting quantum hardware usage by 97.7%.
TRL6 status
Validated in a real operational environment (IBM cloud quantum processor) with autonomous operation, quantifiable performance gains, and confirmation by IBM engineer. The architecture is now ready for integration into quantum cloud platforms and quantum middleware stacks.
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