An Inexact Feasible Quantum Interior Point Method for Linearly Constrained Quadratic Optimization

Abstract

Quantum linear system algorithms (QLSAs) have the potential to speed up algorithms that rely on solving linear systems. Interior Point Methods (IPMs) yield a fundamental family of polynomial-time algorithms for solving optimization problems. IPMs solve a Newton linear system at each iteration to find the search direction, and thus QLSAs can potentially speed up IPMs. Due to the noise in contemporary quantum computers, such quantum-assisted IPM (QIPM) only allows an inexact solution for the Newton linear system. Typically, an inexact search direction leads to an infeasible solution. In our work, we propose an Inexact-Feasible QIPM (IF-QIPM) and show its advantage in solving linearly constrained quadratic optimization problems. We also apply the algorithm to $\ell_1$-norm soft margin support vector machine (SVM) problems and obtain the best complexity regarding dependence on dimension. This complexity bound is better than any existing classical or quantum algorithm that produces a classical solution.