Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems
Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems
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DOI：
10.1016/j.compchemeng.2019.106630
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发表时间：
2020-01-04
影响因子：
4.3
通讯作者：
You, Fengqi
中科院分区：
文献类型：
--
作者：
Ajagekar, Akshay;Humble, Travis;You, Fengqi
量子计算（QC）由于其独特的能力而受到欢迎，这些能力在速度和操作方法方面与经典计算机截然不同。本文提出了混合模型和方法，有效地利用互补的优势，确定性算法和QC技术，以克服组合的复杂性，解决大规模混合整数规划问题。四个应用，即分子构象问题，车间调度问题，制造单元形成问题，和车辆路径问题，具体解决。从分子设计到物流优化的多个尺度上的这些应用问题的大规模实例对于经典计算机上的确定性优化算法来说是计算上的挑战。为了解决计算的挑战，混合基于QC算法提出了广泛的计算实验结果，以证明其适用性和效率。所提出的基于QC的解决方案策略在解决方案质量和计算时间方面具有很高的计算效率，通过利用经典和量子计算机的独特功能。(C)2019爱思唯尔有限公司版权所有。
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. (C) 2019 Elsevier Ltd. All rights reserved.