Kernel discriminant analysis was an important branch in the field of pattern recognition which aimed to expand the ability of linear discriminant analysis to process nonlinear data by kernel function.However,with the exponential growth of data,the classical kernel discriminant analysis algorithm con-sumed a lot of computing resources in extracting features.To solve this problem,a quantum kernel discrim-inant analysis algorithm was proposed based on quantum superposition and parallelism.Firstly,the density operators corresponding to the desired between-class scatter matrix and within-class scatter matrix were con-structed with quantum random access memory technology and controlled rotation operation.Then,the eigenstates were obtained in parallel by incorporating the solution idea of linear equation.Theoretical anal-ysis showed that the algorithm could achieve exponential acceleration compared with the classical algorithm.
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维普
