Classical feature extraction refers to the traditional methods used to identify and select relevant features from data for machine learning models, while quantum feature extraction leverages quantum computing principles to transform and analyze data in potentially more powerful ways. The distinction between the two lies in how each method processes information, with quantum techniques able to explore higher-dimensional spaces more efficiently due to quantum superposition and entanglement.
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