Juq379

QuantumBridge released a public benchmark suite (QBench‑2026) that runs side‑by‑side classical, quantum, and hybrid workloads. Here are the headline numbers (averaged across 5 runs on a single JUQ‑379 module, 4 K operating temperature):

| Benchmark | Classical Baseline (GPU) | JUQ‑379 (Hybrid) | Speed‑up | Energy Efficiency* | |-----------|--------------------------|------------------|----------|--------------------| | Matrix Multiplication (8K×8K) | 0.78 s (NVIDIA H100) | 0.62 s | 1.26× | 1.12× | | Quantum Approximate Optimization Algorithm (QAOA) – Max‑Cut (50‑node) | 12.3 s (IBM Q System One) | 3.1 s | 4.0× | 5.2× | | Hybrid Monte‑Carlo (Finance) | 4.8 s (CPU‑only) | 1.9 s | 2.5× | 2.8× | | Neural‑Network Inference (ResNet‑152) | 12.5 ms (TPU v4) | 10.3 ms | 1.21× | 1.15× | | Mid‑Circuit Error‑Corrected Grover Search (5‑qubit) | 1.4 s (Rigetti Aspen‑10) | 0.38 s | 3.7× | 4.3× |

*Energy efficiency measured as operations per joule at the system level (including cryocooler overhead). juq379

Takeaway: For tasks that can exploit even a small quantum subroutine (e.g., sampling, optimization, linear system solving), JUQ‑379 delivers order‑of‑magnitude speed‑ups while staying competitive on pure classical workloads.

| Tool | Description | |------|-------------| | QBridge Studio | IDE plugin (VS Code/CLion) with live‑debug of hybrid kernels, visual qubit state inspection. | | QBench‑2026 Suite | Standard benchmark set for performance comparison, includes finance, chemistry, optimization, and AI workloads. | | QBridge Marketplace | Repository of pre‑built hybrid kernels (e.g., VQE, QAOA, quantum‑random-number generators). | | Quantum‑Secure TLS | Built‑in lattice‑based TLS library for secure communication between JUQ‑379 nodes. | Takeaway: For tasks that can exploit even a

At the heart of JUW‑379 is the QCI, a low‑latency bus that allows a classical core to issue a “quantum instruction” (e.g., QUBIT_GATE(q0, H)) and instantly receive a measurement result. The round‑trip latency is ≈ 250 ns, a factor of 40× faster than any external quantum‑to‑classical link today.

| Feature | JUQ‑379 | IBM Quantum System Two (Q‑RISC) | Google Sycamore‑X | Rigetti Aspen‑12 | |---------|------------|--------------------------------|-------------------|-----------------| | Hybrid Architecture | On‑die classical + quantum | Separate quantum module (cryostat) | Separate quantum module | Separate quantum module | | Operating Temperature | 4 K (compact cryocooler) | 15 mK (dilution) | 15 mK (dilution) | 15 mK (dilution) | | Qubit Count | 48 transmons | 127 (superconducting) | 54 (superconducting) | 80 (superconducting) | | Gate Fidelity (2‑qubit) | 98.3 % | 99.0 % | 98.5 % | 97.8 % | | Classical Cores | 8× ARM Cortex‑A78AE | None (requires external host) | None | None | | Latency (QC↔CL) | 250 ns (on‑chip) | 10–15 µs (cable) | 12 µs | 13 µs | | Power (incl. cooling) | ~120 W (rack) | ~2 kW (lab) | ~2 kW | ~2 kW | | SDK | QBridge SDK (C++/Python) | Qiskit + OpenQASM | Cirq + JAX | pyQuil | | Target Market | Data‑center & edge | Research labs | Research labs | Research labs | | Tool | Description | |------|-------------| | QBridge

Bottom line: While pure‑quantum machines still have higher qubit counts and marginally better gate fidelity, JUQ‑379 wins on integration, latency, power, and developer friendliness, making it the clear choice for production workloads.