Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems
Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.
Code (0)
등록된 구현이 없습니다.
Tasks
Computational EfficiencyJob Shop SchedulingSchedulingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
When Federated Learning Meets Quantum Computing: Survey and Research Opportunities
Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a sys…
Federated LearningArchitectural Vision for Quantum Computing in the Edge-Cloud Continuum
Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work has yet to draw from research in edge com…
Collaborative InferenceDistributed ComputingEdge-computingImage Classification+1Toward Large-Scale Distributed Quantum Long Short-Term Memory with Modular Quantum Computers
In this work, we introduce a Distributed Quantum Long Short-Term Memory (QLSTM) framework that leverages modular quantum computing to address scalability challenges on Noisy Intermediate-Scale Quantum (NISQ) devices. By …
Hybrid classical-quantum computing: are we forgetting the classical part in the binomial?
The expectations arising from the latest achievements in the quantum computing field are causing that researchers coming from classical artificial intelligence to be fascinated by this new paradigm. In turn, quantum comp…
$Classi|Q\rangle$ Towards a Translation Framework To Bridge The Classical-Quantum Programming Gap
Quantum computing, albeit readily available as hardware or emulated on the cloud, is still far from being available in general regarding complex programming paradigms and learning curves. This vision paper introduces $Cl…