AI-Based Framework for Predicting Quantum State Transitions in Topologically Protected Material/strong>
Authors:-Soundhariya Ravi, Associate Professor Dr S R Raja
Abstract-Quantum state transitions in topologically protected materials have garnered significant attention for their potential applications in quantum computing, spintronics, and material science. Predicting these transitions under varying external conditions remains a challenge due to the intricate interplay of quantum effects and topological invariants. This study proposes an AI-based framework that leverages deep learning techniques to predict quantum state transitions in such materials with high precision. The framework utilizes a custom neural network architecture trained on data derived from simulations and experimental results. By incorporating topological invariants and environmental variables as features, the model accurately predicts phase transitions and provides insights into the factors driving them. The results demonstrate over 95% prediction accuracy, outperforming traditional simulation methods in terms of computational efficiency and scalability. This work lays the foundation for integrating AI into quantum materials research, offering tools for designing next- generation quantum devices.
