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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 and fallback to purely classical execution when quantum error rates exceed a configurable threshold, mitigating potential exploits.

The most pronounced gains appear in workloads that heavily rely on sampling or combinatorial optimization (BERT‑tiny and GNN), confirming the efficacy of the quantum kernels. Power profiling shows that the QCP consumes on average during active phases, with idle power under 0.1 W thanks to an aggressive voltage‑scaling scheme. juq-325

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| Workload | Baseline (ARM Cortex‑A78, 5 W) | JUQ‑325 (Full Heterogeneous) | Energy‑Delay Product (EDP) Improvement | |----------|-------------------------------|------------------------------|----------------------------------------| | MobileNet‑V2 | 3.2 ms latency, 4.1 J energy | , 3.5 J | 1.8× | | BERT‑tiny | 12.4 ms, 9.8 J | 6.7 ms , 7.2 J | 2.1× | | GNN (traffic) | 28.9 ms, 18.0 J | 15.3 ms , 12.3 J | 2.4× |

| Specification | Detail | |---------------|--------| | | 8‑core NPU delivering up to 2 TOPS (tera‑operations per second) for mixed‑precision inference. | | Edge OS | Hardened Linux‑based OS with container support (Docker / OCI) for easy deployment of custom models. | | Model Update Pipeline | OTA (over‑the‑air) model delivery with version rollback and integrity verification via signed manifests. | | Sensor Fusion | Native support for up to 12 simultaneous sensor streams (IMU, LiDAR, camera, temperature, etc.). | | Secure Boot & TPM 2.0 | Guarantees that only authenticated firmware and models run on the device. |

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turns every JUQ‑325 into a smart, autonomous micro‑server that learns, decides, and acts locally, delivering ultra‑low latency, rock‑solid reliability, and built‑in privacy—making the JUQ‑325 the ideal backbone for any edge‑centric deployment.

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