Quantum Machine Learning
Feature maps are mathematical functions that transform input data into a higher-dimensional space, allowing for the extraction of relevant features needed for analysis or classification tasks. This transformation is crucial for quantum machine learning as it helps to exploit the capabilities of quantum systems to uncover hidden patterns and relationships in the data that may not be easily discernible in lower dimensions.
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