Technical Specifications
QL-Series technical specifications: hybrid photonic-classical quantum computing platform. 50-qubit simulation, >99.9% fidelity, multi-tenant support. Desktop form factor, ambient temperature operation.
Technical Specifications
QL-Series Hybrid Photonic-Classical Computing Platform
Platform Overview
The QL-series represents a novel hybrid computing architecture that integrates classical GPU/FPGA acceleration with photonic quantum processing. This desktop-scale, cryogen-free platform delivers simulations of up to ~50 qubits with >99.9% gate fidelity and demonstrated 2–10× performance improvement over conventional classical simulators.
Hybrid Architecture
- Classical GPU/FPGA acceleration layer
- Integrated photonic processing unit (PPU)
- AI-augmented circuit partitioning engine
- Adaptive compiler for dynamic resource allocation
Photonic Subsystem
- Mach-Zehnder interferometer (MZI) mesh network
- Squeezed light sources (>10 dB noise reduction)
- Ambient temperature operation (20-25°C)
- Sub-nanosecond gate execution times
Software Integration
- Native Qiskit, Cirq, PennyLane compatibility
- OpenQASM 3.0 standard compliance
- Comprehensive Python SDK
- Multi-tenant architecture (~100 concurrent sessions)
Performance Specifications
Gate Fidelity
Single and two-qubit operations
Circuit Depth
Maximum gates per circuit
Throughput
Circuit executions per minute
Control Latency
Signal response time
Key Differentiators
- Ambient Temperature Operation: Eliminates costly cryogenic infrastructure and maintenance
- Compact Form Factor: Desktop-scale design optimised for academic and research laboratory environments
- Hybrid Processing: Leverages complementary strengths of classical and quantum computation
- Multi-User Capability: Supports 100+ concurrent users via containerised cloud interface
- Education-First Design: Purpose-built for teaching, training, and research applications
Model Comparison
| Specification | QL-50 (2026) | QL-100 (2027-2028) | QL-X (2029+) |
|---|---|---|---|
| Effective Qubit Capacity | ~50 qubits | ~100 qubits | Scalable distributed |
| Gate Fidelity | >95% (initial), >99% (calibrated) | >99.5% | >99.9% |
| Operating Temperature | 20-25°C ambient | 20-25°C ambient | 20-25°C ambient |
| Cryogenic Requirements | None | None | None |
| Power Consumption | <500W | <800W | <1kW per node |
| Physical Dimensions | 60 × 45 × 30 cm | Rackmount compatible | Distributed architecture |
| Concurrent Users | ~100 | ~500 | 1000+ |
| Target Price (AUD) | $50,000 | $150,000–$250,000 | Enterprise licensing |
Market Comparison
Competitive positioning against established quantum computing platforms for academic and research applications.
| Capability | Quantonic QL-50 | IBM Quantum | Xanadu | IonQ |
|---|---|---|---|---|
| Deployment Model | On-premise | Cloud only | Cloud only | Cloud only |
| Cryogenic Infrastructure | Not required | Required (15mK) | Ambient temp | UHV required |
| Access Latency | Immediate | Hours to days | Minutes to hours | Hours to days |
| Educational Suitability | Purpose-built | Limited access | API only | Restricted |
| Commercial Model | Capital purchase | Subscription | Per-execution | Enterprise contract |
Comparison based on publicly available specifications as of 2025. Subject to vendor updates.
Product Roadmap
Initial Release QL-50 — 2026
Target Market: Universities, educational institutions, early-stage research
- Configuration: NVIDIA Jetson Nano + Xilinx Artix-7 FPGA + 4×4 MZI mesh
- Capacity: ~50 effective qubits, ~100 concurrent users
- Form Factor: Desktop unit (60 × 45 × 30 cm)
- Investment: ~$50,000 AUD
- Applications: Quantum algorithm education, VQE demonstrations, QAOA prototyping, QML research
Production QL-100 — 2027-2028
Target Market: Research institutions, university quantum laboratories, technology startups
- Configuration: RTX-class GPU + enhanced FPGA + 8×8 MZI mesh
- Capacity: ~100 effective qubits, ~500 concurrent users
- Form Factor: Rackmount or enhanced desktop
- Investment: $150,000–$250,000 AUD
- Enhancements: Multi-language SDK (Python, Julia, C++), integrated error correction
Enterprise QL-X — 2029+
Target Market: Enterprise, government, defence, large-scale research programmes
- Architecture: Distributed quantum-classical platform with AI orchestration
- Scalability: QL-Fabric networked architecture, 1000+ concurrent users
- Applications: Drug discovery, financial modelling, logistics optimisation, cryptographic research
- Pricing: Enterprise licensing — contact for consultation
Hardware Configuration (QL-50)
Compute Module
- GPU: NVIDIA Jetson Nano
- CUDA Cores: 128
- Memory: 4GB LPDDR4
- Acceleration: Qiskit Aer, PyTorch inference
FPGA Controller
- Device: Xilinx Artix-7
- Function: Precision timing control
- Interface: DAC/ADC management
- Latency: Sub-microsecond
Optical Assembly
- MZI Network: 4×4 mesh (8×8 capable)
- Light Source: 1550nm telecom laser
- Nonlinear Element: PPLN crystal
- Detection: Single-photon detectors
Network Infrastructure
- Connectivity: WiFi 6E
- Isolation: VLAN per session
- Containerisation: Docker-based
- Management: Intel NUC / Raspberry Pi