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Quantum Annealing Combinatorial Optimization (QACO)

Jincheng Zhang

Published Sep 3, 2026• 0 citations
Publisher

Abstract

This paper explores the application of Quantum Annealing (QA) to the field of combinatorial optimization. The core idea is to leverage the principles of quantum mechanics, specifically quantum tunneling, to efficiently solve complex optimization problems. We present a framework for utilizing QA to tackle problems represented as quadratic unconstrained binary optimization (QUBO) models. The methodology focuses on mapping the problem's constraints and objective function into a form suitable for QA hardware. We discuss the challenges involved in this mapping process, including the limitations of current QA devices and the need for careful problem formulation. Furthermore, we analyze the potential advantages of QA over classical optimization techniques, particularly concerning the exploration of a wider solution space. The research highlights the promise of QACO as a viable approach for tackling computationally demanding combinatorial optimization tasks, offering a novel method to enhance optimization efficiency.

DOI
10.5281/zenodo.22271313

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This paper investigates the application of quantum annealing to solve combinatorial optimization problems. The core claim is that quantum annealing algorithms can effectively enhance the efficiency and accuracy of solving such problems. The underlying mechanism involves simulating the quantum annealing process, leveraging the quantum tunneling effect to explore the solution space and identify optimal solutions. This work presents a novel approach by applying quantum annealing to combinatorial optimization, offering a fresh perspective on problem-solving. We explore the theoretical foundations, model the annealing process, and discuss potential challenges and future research directions. The focus is on demonstrating the feasibility and potential benefits of this approach rather than presenting results from experimental implementations.
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