Formulating and Solving Routing Problems on Quantum Computers

被引:53
|
作者
Harwood S. [1 ]
Gambella C. [2 ]
Trenev D. [1 ]
Simonetto A. [2 ]
Bernal D. [3 ]
Greenberg D. [4 ]
机构
[1] Corporate Strategic Research, ExxonMobil Research and Engineering, Annandale, 08801, NJ
[2] Ibm Quantum, Ibm Research Ireland
[3] Quantum Computing Group, Carnegie Mellon University, Pittsburgh, 15213, PA
[4] Ibm Quantum, Ibm Thomas J. Watson Research Center, Yorktown Heights, 10598, NY
关键词
Optimization; quantum computing; routing; variational algorithms;
D O I
10.1109/TQE.2021.3049230
中图分类号
学科分类号
摘要
The determination of vehicle routes fulfilling connectivity, time, and operational constraints is awell-studied combinatorial optimization problem. The NP-hard complexity of vehicle routing problems has fostered the adoption of tailored exact approaches, matheuristics, and metaheuristics on classical computing devices. The ongoing evolution of quantum computing hardware and the recent advances of quantum algorithms (i.e., VQE, QAOA, and ADMM) for mathematical programming make decision-making for routing problems an avenue of research worthwhile to be explored on quantum devices. In this article, we propose several mathematical formulations for inventory routing cast as vehicle routing with time windows and comment on their strengths and weaknesses. The optimization models are compared from a quantum computing perspective, specifically with metrics to evaluate the difficulty in solving the underlying quadratic unconstrained binary optimization problems. Finally, the solutions obtained on simulated quantum devices demonstrate the relative benefits of different algorithms and their robustness when put into practice. © 2022 IEEE.
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