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

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

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 com...

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Bibliographic Details
Main Authors: Zeguan Wu, Mohammadhossein Mohammadisiahroudi, Brandon Augustino, Xiu Yang, Tamás Terlaky
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Entropy
Subjects:
quantum computing
interior point method
quadratic optimization
Online Access:https://www.mdpi.com/1099-4300/25/2/330

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