Quantum-enhanced Regression Analysis Using State-of-the-art QLSAs and QIPMs

Abstract

Quantum computing has the potential to speed up machine learning methods. One major direction is using quantum linear algebra to solve linear system problems or optimization problems in the machine learning area. Quantum approaches in the literature for different types of least squares problems demonstrate speedups w.r.t. dimension but have disadvantages w.r.t. precision and condition number. In this paper, we discuss how an iterative refinement scheme can deliver an accurate solution without the excessive cost of Quantum Linear System Algorithms (QLSAs). In addition, we propose an adaptive regularization approach that can mitigate the effect of condition number on solution time. In the second part of this paper, we investigate how state-of-the-art Quantum Interior Point Methods (QIPMs) can solve more sophisticated regression problems such as Lasso regression and support vector machine problems, which can be reformulated as quadratic optimization problems.