Feedback-Based Quantum Algorithm for Constrained Optimization Problems
Abstract
The feedback-based algorithm for quantum optimization (FALQON) has recently been proposed to solve quadratic unconstrained binary optimization problems. This paper efficiently generalizes FALQON to tackle quadratic constrained binary optimization (QCBO) problems. For this purpose, we introduce a new operator that encodes the problem's solution as its ground state. Using Lyapunov control theory, we design a quantum control system such that the state converges to the ground state of this operator. When applied to the QCBO problem, we show that our proposed algorithm saves computational resources by reducing the depth of the quantum circuit and can perform better than FALQON. The effectiveness of our proposed algorithm is further illustrated through numerical simulations.
Cite
@article{arxiv.2406.08169,
title = {Feedback-Based Quantum Algorithm for Constrained Optimization Problems},
author = {Salahuddin Abdul Rahman and Özkan Karabacak and Rafal Wisniewski},
journal= {arXiv preprint arXiv:2406.08169},
year = {2025}
}
Comments
This paper is accepted for publication in the 15th International Conference on Parallel Processing and Applied Mathematics (PPAM 2024)