Quantum Telemetry Dashboard is a production-grade quantum control and telemetry visualization platform designed for real-time monitoring of cryogenic systems. It combines a high-fidelity physics simulator with a high-performance frontend to train physicists and validate control systems before deployment with actual quantum hardware.
- 128-channel simultaneous simulation at 50Hz sustained rate (6,400 samples/sec)
- Advanced noise models: Ornstein-Uhlenbeck (thermal drift), Lorentzian charge noise, Hyperfine 1/f² noise, TLS 1/f noise, Johnson noise
- Spatial correlation via Cholesky decomposition simulating real sensor crosstalk
- 12+ anomaly types: Quench, PLL unlock, EMI burst, Flux creep, Ground loop, and more
- 60fps rendering with Canvas 2D and uPlot
- Ribbon Heatmap: 128-channel time-series heatmap with GPU-accelerated scrolling
- Real-time Charts: Time-series line charts with LTTB downsampling
- Spectrum Analyzer: FFT visualization for frequency analysis
- CryoStack: Animated thermal gauge display for 4K, 1K, Cold Plate, and Mixing Chamber stages
- Docker Compose orchestration with health checks
- WebSocket real-time streaming with binary Protobuf serialization
- JWT authentication with 15-minute expiry
- TimescaleDB for time-series storage with automatic compression
- Redis for message queuing and backpressure handling
┌─────────────────────────────────────────────────────────────────────┐
│ User / Browser │
│ (React SPA + uPlot + Canvas) │
└──────────────────────────────┬──────────────────────────────────────┘
│
│ WebSocket (Protobuf)
▼
┌─────────────────────────────────────────────────────────────────────┐
│ Backend (FastAPI) │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Physics │→ │ Correlation │→ │ Calibration │→ │ Broadcast │ │
│ │ Simulator │ │ Matrix │ │ Engine │ │ (WS) │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
│ │ │ │ │ │
│ └────────────────┴────────┬───────┴────────────────┘ │
│ ▼ │
│ ┌───────────────────────────┐ │
│ │ Redis Pub/Sub + Streams │ │
│ └────────────┬──────────────┘ │
│ │ │
│ ┌──────────────▼──────────────┐ │
│ │ TimescaleDB │ │
│ │ (Time-series storage) │ │
│ └─────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
| Layer | Technology | Purpose |
|---|---|---|
| Frontend | React 19 + TypeScript | UI Framework |
| Frontend | Vite | Build tool with HMR |
| Frontend | uPlot | Canvas-based charting |
| Frontend | Zustand | State management |
| Frontend | Framer Motion | Animation library |
| Backend | Python 3.11 | Runtime |
| Backend | FastAPI | Web framework |
| Backend | NumPy | Vectorized physics simulation |
| Protocol | Protobuf | Binary serialization |
| Database | TimescaleDB | Time-series storage |
| Broker | Redis | Message queue |
| Proxy | Nginx | WebSocket upgrade, SSL |
| Container | Docker Compose | Orchestration |
- Docker (20.10+) with Docker Compose
- Python 3.11+ (for local development)
- Node.js 20+ (for frontend development)
- Port availability: 80, 8000, 5432, 6379
# Clone the repository
git clone https://github.com/sirusdas/quantium-dashboard.git
cd quantium-dashboard
# Start the complete stack
docker-compose up --build
# Access the dashboard
# Frontend: http://localhost
# Backend API: http://localhost:8000
# API Docs: http://localhost:8000/docscd backend
# Create virtual environment
python -m venv venv
source venv/bin/activate # Linux/Mac
# or: venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txt
# Run the server
python -m uvicorn main:app --reload --port 8000cd frontend
# Install dependencies
npm install
# Start development server
npm run devThe application uses the following environment variables (configured in docker-compose.yml):
| Variable | Default | Description |
|---|---|---|
REDIS_URL |
redis://redis:6379 |
Redis connection string |
TIMESCALE_URL |
postgres://postgres:password@timescaledb:5432/telemetry |
TimescaleDB connection |
quantium-dashboard/
├── backend/ # Python FastAPI Backend
│ ├── config/ # Configuration loaders
│ ├── db/ # TimescaleDB connection
│ ├── models/ # Pydantic models
│ ├── pipeline/ # Data processing (calibration)
│ ├── simulator/ # Physics engine
│ │ ├── physics.py # Noise generation
│ │ ├── correlation.py # Spatial correlation
│ │ └── anomalies.py # Fault injection
│ ├── transport/ # WebSocket & Redis
│ ├── auth.py # JWT authentication
│ ├── audit.py # Audit logging
│ └── main.py # Telemetry loop (50Hz)
├── frontend/ # React + Vite Frontend
│ ├── src/
│ │ ├── components/ # UI components
│ │ │ ├── CryoStack.tsx # Thermal gauges
│ │ │ ├── RealtimeChart.tsx # uPlot chart
│ │ │ ├── RibbonHeatmap.tsx # Canvas heatmap
│ │ │ └── SpectrumAnalyzer.tsx
│ │ ├── hooks/ # React hooks
│ │ ├── workers/ # Web Workers
│ │ ├── store/ # Zustand stores
│ │ └── proto/ # Generated Protobuf
│ └── package.json
├── proto/ # Protocol Buffer definitions
│ └── telemetry.proto
├── docker-compose.yml # Container orchestration
├── nginx.conf # Reverse proxy config
├── Dockerfile.backend # Backend container
├── Dockerfile.frontend # Frontend container
└── architecture/ # Detailed documentation
| Endpoint | Method | Description |
|---|---|---|
/api/v1/auth/login |
POST | JWT token login |
/api/v1/auth/me |
GET | Current user info |
| Endpoint | Method | Description |
|---|---|---|
/ws |
WebSocket | Real-time telemetry stream |
/api/v1/telemetry/status |
GET | Connection status |
| Endpoint | Method | Description |
|---|---|---|
/api/v1/inject/anomaly |
POST | Inject fault condition |
Example Anomaly Request:
{
"type": "quench",
"target_channels": [0, 1, 2],
"spatial_pattern": "zone",
"severity": 0.8,
"duration_ms": 5000
}| Metric | Target | Actual |
|---|---|---|
| Streaming Rate | 128 ch × 50Hz | 50Hz sustained |
| UI Framerate | 60 fps | 60fps |
| Latency (E2E) | <100ms | <100ms |
| CPU (simulator) | <30% | ~20% |
| Memory Growth | O(1) | Constant (circular buffers) |
- JWT authentication with RS256 signing
- WebSocket origin validation
- Structured JSON audit logging
- Data integrity via per-sample checksums
- TLS-ready (Nginx terminates SSL)
Detailed architecture documentation is available in the architecture/ directory:
- Overview.md - High-level system architecture
- Tech_Stack.md - Technology choices & rationale
- Backend.md - Backend deep-dive (physics, transport)
- Frontend.md - Frontend components & data flow
- File_Structure.md - Complete directory map
# Physics validation (KS tests)
cd tests
python test_physics.py
# Load testing
cd tests
node test_physics.pyContributions are welcome! Please read our contributing guidelines before submitting PRs.
- Fork the repository
- Create a feature branch
- Make your changes with tests
- Submit a pull request
MIT License - See LICENSE for details.
Built with contributions from the quantum computing research community. Designed to meet the rigorous requirements of production-grade quantum control facilities.
Production-Grade Quantum Telemetry
Built for physicists, by engineer