Quantum Discriminant Analysis for Dimensionality Reduction and Classification
Journal Article - Open Access
University of California, Los Angeles Los Angeles United States
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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.
- Quantum Theory and Relativity