Improvements to Quantum Interior Point Method for Linear Optimization
Abstract
Quantum linear system algorithms (QLSA) have the potential to speed up Interior Point Methods (IPM). However, a major challenge is that QLSAs are inexact and sensitive to the condition number of the coefficient matrices of linear systems. This sensitivity is exacerbated when the Newton systems arising in IPMs converge to a singular matrix. Recently, an Inexact Feasible Quantum IPM (IF-QIPM) has been developed that addresses the inexactness of QLSAs and, in part, the influence of the condition number using iterative refinement. However, this method requires a large number of gates and qubits to be implemented. Here, we propose a new IF-QIPM using the normal equation system, which is more adaptable to near-term quantum devices. To mitigate the sensitivity to the condition number, we use preconditioning coupled with iterative refinement to obtain better gate complexity. Finally, we demonstrate the effectiveness of our approach on IBM Qiskit simulators
Keywords
Cite
@article{arxiv.2310.07574,
title = {Improvements to Quantum Interior Point Method for Linear Optimization},
author = {Mohammadhossein Mohammadisiahroudi and Zeguan Wu and Brandon Augustino and Arriele Carr and Tamás Terlaky},
journal= {arXiv preprint arXiv:2310.07574},
year = {2023}
}