Juq-325

Quantum‑enabled inference raises novel attack surfaces: adversaries could attempt to manipulate the quantum state (e.g., via electromagnetic interference) to degrade accuracy. JUQ‑325 incorporates real‑time fidelity monitoring and fallback to purely classical execution when quantum error rates exceed a configurable threshold, mitigating potential exploits.

| Industry | Example Application | |----------|----------------------| | Manufacturing | Detect equipment wear in real time, trigger preventive maintenance before failure. | | Smart Buildings | Optimize HVAC and lighting based on occupancy patterns without sending personal data to the cloud. | | Healthcare | Run on‑device ECG or imaging analysis at the bedside, delivering instant alerts while keeping patient data local. | | Logistics | Dynamically reroute autonomous forklifts around obstacles using on‑board perception. | | Retail | Provide instant, privacy‑preserving customer behavior insights for in‑store promotions. | juq-325

When contrasted with a state‑of‑the‑art edge AI ASIC (e.g., Google Edge TPU v3), JUQ‑325 matches or exceeds performance on the same power envelope, while offering algorithmic flexibility: developers can toggle quantum kernels on a per‑model basis without redesigning hardware. JUQ‑325 is built around three tightly coupled subsystems:

JUQ‑325 is built around three tightly coupled subsystems: Google Edge TPU v3)

The overall chip area is 45 mm² in a 7 nm FinFET process, with an additional 8 mm² photonic back‑end‑of‑line (BEOL) for the quantum subsystem.