Evaluating the Convergence of Tabu Enhanced Hybrid Quantum Optimization
Enrico Blanzieri, Davide Pastorello, Valter Cavecchia, Alexander Rumyantsev, Maria Maltseva · arXiv (Cornell University) · 2022
In this paper we introduce the Tabu Enhanced Hybrid Quantum Optimization metaheuristic approach useful for optimization problem solving on a quantum hardware. We address the theoretical convergence of the proposed scheme from the viewpoint of the collisions in the object which stores the tabu states, based on the Ising model. The results of numerical evaluation of the algorithm on quantum hardware as well as on a classical semiconductor hardware model are also demonstrated.