From Theory to Practice: Analyzing Variational Quantum Power Method for Quantum Optimization of QUBO Problems
Abstract
The variational quantum power method (VQPM), which adapts the classical power iteration algorithm for quantum settings, has shown promise for eigenvector estimation and optimization on quantum hardware. In this work, we provide a comprehensive theoretical and numerical analysis of VQPM by investigating its convergence, robustness, and qubit locking mechanisms. We present detailed strategies for applying VQPM to QUBO problems by leveraging these locking mechanisms. Based on the simulations for each strategy we have carried out, we give systematic guidelines for their practical applications. We also offer a numerical comparison with the quantum approximate optimization algorithm (QAOA) by running both algorithms on a set of trial problems and a simulation on noisy environments by using IBM Qiskit Aer simulation framework. Our results indicate that VQPM can be employed as an effective quantum optimization algorithm on quantum computers for QUBO problems, and this work can serve as an initial guideline for such applications.
Keywords
Cite
@article{arxiv.2505.12990,
title = {From Theory to Practice: Analyzing Variational Quantum Power Method for Quantum Optimization of QUBO Problems},
author = {Ammar Daskin},
journal= {arXiv preprint arXiv:2505.12990},
year = {2026}
}
Comments
A simulation for noisy environments is added by using IBM qiskit aer. All simulation code is publicly available at https://github.com/adaskin/vqpm