Work

Quantum Algorithm for Spectral Regression for Regularized Subspace Learning

Year: 2018

Type: article

Abstract: In this paper, we propose an efficient quantum algorithm for spectral regression which is a dimensionality reduction framework based on the regression and spectral graph analysis. The quantum algorithm involves two core subroutines: the quantum principal eigenvectors analysis and the quantum ridge regression algorithm. The quantum principal eigenvectors analysis can be performed by an efficient sparse Hamiltonian simulation. For the ridge regression, we propose a quantum algorithm that is derived from the quantum singular value decomposition method. Our quantum ridge regression algorithm is more suitable for data matrices that are non-sparse and skewed. Our analysis demonstrates that the quantum subroutines can be implemented with an approximatively polynomial speedup on a quantum computer over their classical counterparts.

Source: IEEE Access

DOI: 10.1109/access.2018.2886581

Authors: Fan-Xu Meng, Xu-Tao Yu, Rui-Qing Xiang, Zai-Chen Zhang

Institutions: State Key Laboratory of Millimeter Waves, Southeast University, University of California, Berkeley

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: National Natural Science Foundation of China