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The Spin-Based Neural Computation Framework is an advanced simulation library that combines quantum mechanics, reinforcement learning, and energy-based neural networks to study and model physical phenomena. Designed for researchers in AI, scientific computing, and statistical physics, this framework explores spin systems—such as the Ising, Kitaev, and disordered models—through machine learning and physics-informed neural networks. It has wide-ranging applications in quantum computing, materials science, and artificial intelligence, making it a versatile and powerful tool.