Help Center
Detailed documentation and legal resources for QNeuroAI.
Tech Stack
Technologies powering the platform.
Frontend
Built with Next.js 14 (App Router), TypeScript, and Tailwind CSS for a responsive, modern interface. Uses generic components and client-side logic for real-time interactions.
Backend & AI
Python microservice architecture using FastAPI. Core storage via PostgreSQL/Prisma. AI/ML pipeline utilizes TensorFlow 2.x and PennyLane for Hybrid Quantum-Classical analysis.
Architecture
The system employs a Hybrid Quantum-Classical 3D-CNN. CT/MRI images are preprocessed (Windowing/Z-Score), passed through a 3D-CNN backbone for feature extraction, reduced to a quantum-compatible state space, and processed by a Variational Quantum Circuit (VQC) for high-sensitivity anomaly detection.
Usage Guide
- Upload: Navigate to "New Diagnostic", upload NIfTI/DICOM file (.nii, .nii.gz, .dcm).
- Analyze: Select Scan Type (MRI/CT). The system auto-detects modality mismatches. Click Analyze.
- Results: View 3D visualization, anomaly annotations, and download PDF reports.
Settings
Configure organization details, user access controls (RBAC), and default scan parameters in the Admin dashboard.
Financials
Manage subscription tiers (Basic, Pro, Enterprise), view billing history, and allocate credits for Quantum Processing Unit (QPU) hours.