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Electronic-Structure-Informed Many-Body Encoding for Quantum Machine Learning
Data encoding is a central challenge in quantum machine learning (QML), as it determines how classical information is mapped onto quantum states and influences the expressive power and efficiency of parameterized quantum circuits. Here, we introduce a physics-informed quantum encoding strategy that
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