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Quantum Annealing for Combinatorial Optimization with Time-Dependent Constraints

Jincheng Zhang

Published Aug 29, 2026• 0 citations
Publisher

Abstract

This paper investigates the application of quantum annealing to combinatorial optimization problems characterized by time-dependent constraints. Current quantum annealing algorithms often face limitations when dealing with such scenarios, necessitating adaptive approaches. We propose a novel quantum annealing algorithm incorporating a dynamic constraint solver. This solver allows the system to continuously monitor and adjust its solution trajectory based on the evolving nature of the constraints. The core mechanism involves encoding the problem into an appropriate Hamiltonian and utilizing the quantum annealing process to minimize the energy. We aim to broaden the applicability of quantum annealing beyond static constraint problems, offering a more robust solution for a wider range of real-world applications. The theoretical framework is presented, highlighting the key components and their interactions. We explore the potential advantages and challenges of this dynamic approach.

DOI
10.5281/zenodo.22154401

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