AI Agents Assisted Platform for Secure Quantum-Classical Networking Education

Abstract

This paper presents Sonata, an AI-agent-assisted platform designed to address the challenges of educating students in secure quantum-classical hybrid networking. By integrating a multi-agent AI framework with a quantum-classical network simulator and a learning objects repository, Sonata offers a hands-on, interactive learning environment. The platform leverages large language models to provide conversational interfaces, personalized feedback, and scenario generation, enabling students to explore complex quantum networking concepts without requiring extensive prior expertise. The system includes a user-friendly GUI for designing network topologies, a Python API for programmatic control, and educational modules tailored to various knowledge levels. Sonata aims to bridge the gap in quantum education by making transdisciplinary concepts accessible, fostering experiential learning, and preparing a workforce proficient in quantum information sciences.