Quantum Discriminant Analysis for Dimensionality Reduction and Classification

Understand

We present quantum algorithms to efficiently perform discriminant analysis for dimensionality reduction and classification over an exponentially large input data set.

• Compared with the best-known classical algorithms, the quantum algorithms show an exponential speedup in both the number of training vectors $M$ and the feature space dimension $N$.
• We generalize the previous quantum algorithm for solving systems of linear equations [Phys.
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