Quantum Laplacian Eigenmaps is a technique used for dimensionality reduction that leverages quantum mechanics principles to analyze and represent high-dimensional data in a lower-dimensional space. This method connects the classical Laplacian eigenmaps approach with quantum computing, utilizing quantum states to enhance the representation of the data structure, leading to better insights and more efficient processing.
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