Villar-Rodriguez, EstherOsaba, EnekoOregi, IzaskunIshibuchi, HisaoKwoh, Chee-KeongTan, Ah-HweeSrinivasan, DiptiMiao, ChunyanTrivedi, AnupamCrockett, Keeley2024-07-242024-07-242022Villar-Rodriguez , E , Osaba , E & Oregi , I 2022 , Analyzing the behaviour of D'WAVE quantum annealer : Fine-tuning parameterization and tests with restrictive Hamiltonian formulations . in H Ishibuchi , C-K Kwoh , A-H Tan , D Srinivasan , C Miao , A Trivedi & K Crockett (eds) , Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 . Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 , Institute of Electrical and Electronics Engineers Inc. , pp. 938-946 , 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 , Singapore , Singapore , 4/12/22 . https://doi.org/10.1109/SSCI51031.2022.10022300conference9781665487689https://hdl.handle.net/11556/1874Publisher Copyright: © 2022 IEEE.Despite being considered as the next frontier in computation, Quantum Computing is still in an early stage of development. Indeed, current commercial quantum computers suffer from some critical restraints, such as noisy processes and a limited amount of qubits, among others, that affect the performance of quantum algorithms. Despite these limitations, researchers have devoted much effort to propose different frameworks for efficiently using these Noisy Intermediate-Scale Quantum (NISQ) devices. One of these procedures is D'WAVE Systems' quantum-annealer, which can be used to solve optimization problems by translating them into an energy minimization problem. In this context, this work is focused on providing useful insights and information into the behaviour of the quantum-annealer when addressing real-world combinatorial optimization problems. Our main motivation with this study is to open some quantum computing frontiers to non-expert stakeholders. To this end, we perform an extensive experimentation, in the form of a parameter sensitive analysis. This experimentation has been conducted using the Traveling Salesman Problem as benchmarking problem, and adopting two QUBOs: state-of-the-art and a heuristically generated. Our analysis has been performed on a single 7-noded instance, and it is based on more than 200 different parameter configurations, comprising more than 3700 unitary runs and 7 million of quantum reads. Thanks to this study, findings related to the energy distribution and most appropriate parameter settings have been obtained. Finally, an additional study has been performed, aiming to determine the efficiency of the heuristically built QUBO in further TSP instances.9enginfo:eu-repo/semantics/openAccessconference output10.1109/SSCI51031.2022.10022300D'WAVEOptimizationQuantum AnnealerQuantum ComputingTraveling Salesman ProblemArtificial IntelligenceComputer Science ApplicationsDecision Sciences (miscellaneous)Computational MathematicsControl and OptimizationTransportationhttp://www.scopus.com/inward/record.url?scp=85147793091&partnerID=8YFLogxK