Towards solving large QUBO problems using quantum algorithms: improving the LogQ scheme
Abstract
The LogQ algorithm encodes Quadratic Unconstrained Binary Optimization (QUBO) problems with exponentially fewer qubits than the Quantum Approximate Optimization Algorithm (QAOA). The advantages of conventional LogQ are accompanied by a challenge related to the optimization of its free parameters, which requires the usage of resource intensive evolutionary or even global optimization algorithms. We propose a new LogQ parameterization that can be optimized with a gradient-inspired method, which is less resource-intensive and thus strengthens the advantage of LogQ over QAOA for large/industrial problems. We illustrate the features of our method on an analytical model and present larger scale numerical results on MaxCut problems.
Cite
@article{arxiv.2507.08489,
title = {Towards solving large QUBO problems using quantum algorithms: improving the LogQ scheme},
author = {Yagnik Chatterjee and Jérémie Messud},
journal= {arXiv preprint arXiv:2507.08489},
year = {2025}
}
Comments
Accepted 2025 IEEE Quantum Week. Personal use of this material permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works