Quantum principal component analysis based on the dynamic selection of eigenstates

Abstract

Abstract Quantum principal component analysis is a dimensionality reduction method to select the significant features of a dataset. A classical method finds the solution in polynomial time, but when the dimension of feature space scales exponentially, it is inefficient to compute the matrix exponentiation of the covariance matrix. The quantum method uses density matrix exponentiation to find principal components with exponential speedup. We enhance the existing algorithm that applies amplitude amplification using range-based static selection of eigenstates on the output of phase estimation. So, we propose an equivalent quantum method with the same complexity using a dynamic selection of eigenstates. Our algorithm can efficiently find phases of equally likely eigenvalues based on the similarity scores. It obtains principal components associated with highly probable larger eigenvalues. We analyze these methods on various factors to justify the resulting complexity of a proposed method as effective in quantum counterparts.