Principal Component Analysis (PCA) is a statistical technique used to simplify complex datasets by transforming them into a lower-dimensional space while preserving as much variance as possible. This process helps in identifying patterns, reducing noise, and visualizing high-dimensional data, making it a valuable tool in data analysis and machine learning, especially when implementing quantum algorithms like the Quantum Support Vector Machine (QSVM).
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