Variational Quantum Algorithms for Dimensionality Reduction and Classification

10/27/2019
by   Jin-Min Liang, et al.
0

Dimensionality reduction and classification play an absolutely critical role in pattern recognition and machine learning. In this work, we present a quantum neighborhood preserving embedding and a quantum local discriminant embedding for dimensionality reduction and classification. These two algorithms have an exponential speedup over their respectively classical counterparts. Along the way, we propose a variational quantum generalized eigenvalue solver (VQGE) that finds the generalized eigenvalues and eigenvectors of a matrix pencil (G,S) with coherence time O(1). We successfully conduct numerical experiment solving a problem size of 2^5×2^5. Moreover, our results offer two optional outputs with quantum or classical form, which can be directly applied in another quantum or classical machine learning process.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset