| Generation | Target Release | Key Improvements | |------------|----------------|------------------| | JUQ‑804‑UC (2028) | 2‑U, > 30 k logical qubits, 99.9999 % gate fidelity | | JUQ‑905‑LC (2030) | 1‑U “Laser‑Cooled” architecture using trapped‑ion‑photonic hybrid, fully room‑temperature control electronics | | JUQ‑X‑QC (2032) | Modular quantum‑fabric with inter‑processor entanglement enabling distributed logical qubits across data‑center clusters |
The roadmap emphasizes modularity, energy efficiency, and heterogeneous integration (e.g., combining superconducting qubits with photonic interconnects).
The processor implements a surface‑code logical qubit architecture with: JUQ-703-UC
| Benchmark | Classical (CPU/GPU) | JUQ‑703‑UC (Logical) | Speed‑up | |-----------|----------------------|----------------------|----------| | Random‑Circuit Sampling (RCS), 64‑qubit depth 30 | 3 × 10⁶ core‑hours (Summit) | 15 seconds (wall‑time) | ~7 × 10⁸× | | Fermi‑Hubbard model (10 × 10 lattice) | 1 month (Titan V) | 2 hours (wall‑time) | ~360× | | Traveling‑Salesman (10 000 cities) | Approx. optimal (branch‑and‑bound) – 72 h | Near‑optimal (≈ 1 % gap) – 5 min | > 800× | | Quantum Phase Estimation (QPE) on LiH molecule | 12 h (GPU‑accelerated) | 0.7 s (wall‑time) | ~6 × 10⁴× |
The benchmarks were performed by the European Quantum Benchmarking Consortium (EQBC) under peer‑reviewed conditions and are publicly available on the OpenQuantumBench repository. | Generation | Target Release | Key Improvements
JUQ-703-UC is a compact, wall-mounted (or rack-adaptable) uninterruptible power device designed to provide short-duration backup power and power-conditioning for small network equipment, single workstations, or point-of-sale systems. It combines a line-interactive inverter topology with surge protection and basic management features aimed at small offices, retail, and edge-network deployments.
| Domain | Use‑Case | Impact | |--------|----------|--------| | Quantum Chemistry | Simulating strongly correlated electron systems (e.g., transition‑metal catalysts). | Reduces classical compute time from weeks to hours, enabling rapid discovery of greener processes. | | Optimization | Solving large‑scale combinatorial problems (e.g., supply‑chain routing, portfolio optimization). | Provides near‑optimal solutions for problems beyond the reach of classical heuristics. | | Machine Learning | Implementing quantum‑enhanced kernel methods and variational quantum classifiers. | Achieves higher expressive power with fewer parameters, leading to faster training on noisy datasets. | | Materials Science | Modeling quantum phase transitions in high‑Tc superconductors. | Accelerates the identification of novel materials with target electronic properties. | | Cryptography | Testing post‑quantum algorithms against realistic quantum adversaries. | Supplies a trustworthy benchmark for NIST‑PQC candidates. | | Fundamental Physics | Simulating lattice gauge theories and quantum gravity analogues. | Offers a controllable platform to test theoretical predictions at scales inaccessible to particle accelerators. | | Benchmark | Classical (CPU/GPU) | JUQ‑703‑UC (Logical)
Because the JUQ‑703‑UC is rack‑mountable, it can be integrated into existing data‑center infrastructures, opening quantum acceleration to enterprises that previously required dedicated cryogenic labs.
| Feature | Specification | Benefit |
|---------|----------------|---------|
| Processor | ARM Cortex‑M7, 400 MHz, 1 MB flash, 256 KB SRAM | Fast deterministic execution for real‑time control loops |
| Operating Voltage | 3.3 V ±5 % (regulated) | Compatibility with standard industrial power supplies |
| Digital I/O | 32 GPIO pins (5 V‑tolerant), 8 PWM channels (up to 20 kHz) | Direct drive of actuators, LEDs, relays, and encoders |
| Analog I/O | 8 × 16‑bit ADC (up to 1 MS/s), 4 × 12‑bit DAC | High‑resolution acquisition of sensors and precise analog output |
| Communication Interfaces | - CAN‑FD (2 Mbps)
- RS‑485 (UART)
- Ethernet 10/100 Mbps (with PoE)
- USB‑C (Device/Host)
- SPI, I²C, LIN | Seamless integration into legacy and modern networks |
| Wireless Options (via optional module) | Wi‑Fi 802.11b/g/n, Bluetooth 5.0, Thread/Z‑Wave | Enables remote monitoring and OTA updates |
| Real‑Time Operating System | FreeRTOS‑based firmware with deterministic scheduler | Simplifies development of time‑critical applications |
| Security | Hardware‑rooted secure boot, AES‑256 encryption, TPM 2.0 | Protects firmware integrity and data privacy |
| Environmental Rating | - Operating Temp: –40 °C to +85 °C
- IP67 enclosure (when mounted in the supplied housing) | Suitable for harsh indoor, outdoor, and marine environments |
| Power Management | Dynamic voltage scaling, low‑power sleep modes (down to 10 µA) | Extends battery life for remote deployments |
| Development Tools | - Eclipse‑based IDE with JTAG/SWD debugger
- Pre‑compiled driver libraries (C/C++)
- Python API for rapid prototyping | Accelerates time‑to‑market for both firmware engineers and system integrators |
| Parameter | Value | Remarks | |-----------|-------|---------| | Transmon type | Fixed‑frequency + tunable coupler | Provides both static and dynamic connectivity. | | Transition frequency (f₀₁) | 4.8–5.2 GHz (distribution) | Designed to avoid spectral collisions. | | Anharmonicity | –300 MHz | Ensures selective driving of the |0⟩→|1⟩ transition. | | Coherence times | T₁ ≈ 120 µs, T₂ ≈ 150 µs (median) | Result of 3D‑cavity packaging & surface‑treatment. | | Readout resonator | 6.8 GHz, Q ≈ 10⁴ | Supports multiplexed dispersive readout (up to 32 qubits per feedline). |
Each transmon sits on a silicon‑on‑insulator (SOI) substrate with a TiN‑based planar capacitor and a NbN‑based Josephson junction. The fabrication flow uses deep‑UV lithography (193 nm) and atomic‑layer‑deposited (ALD) aluminum oxide for dielectric passivation, reducing two‑level‑system (TLS) loss.