Quantum Youth Vulnerability Classifier: Integrated AI-Quantum System for Young Population Risk Analysis

Specialized SKILL Combining Quantum Computing Principles with Youth-Specific Vulnerability Assessment This hybrid computational tool merges quantum-inspired classification algorithms with domain-specific youth vulnerability indicators, creating a powerful system for analyzing risks facing young populations in education, employment, and social inclusion contexts. Key Innovations: Quantum-inspired multi-agent modeling of youth development trajectories; simultaneous assessment of educational, economic, health, and social vulnerability dimensions; probabilistic prediction of intervention success rates; adaptive learning from program outcome data to improve classification accuracy over time. Potential Use Scenarios: Can support large-scale youth policy initiatives by governments; enable NGOs to optimize resource distribution in youth programs; facilitate academic longitudinal studies on youth transitions; power AI-driven career guidance systems; assist in designing evidence-based youth employment programs; support early intervention in dropout risk detection. Impact Possibilities: May improve targeting precision in youth social programs; could reduce administrative costs through automation; enables data-driven youth policy design; supports transparent allocation of youth development resources; facilitates cross-program learning and best practice identification. Developed at UFMT (Universidade Federal de Mato Grosso). CAPES/CNPq PTT classification: SKILL (AI-powered computational tool). Part of this content may have been AI-generated under supervision by Daniel Maciel and Arturo Zavala.