TY - JOUR
T1 - Formulating and Solving Routing Problems on Quantum Computers
AU - Harwood, Stuart
AU - Gambella, Claudio
AU - Trenev, Dimitar
AU - Simonetto, Andrea
AU - Bernal, David
AU - Greenberg, Donny
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
KW - Optimization
KW - quantum computing
KW - routing
KW - variational algorithms
U2 - 10.1109/TQE.2021.3049230
DO - 10.1109/TQE.2021.3049230
M3 - Article
AN - SCOPUS:85189043250
SN - 2689-1808
VL - 2
JO - IEEE Transactions on Quantum Engineering
JF - IEEE Transactions on Quantum Engineering
M1 - 3100117
ER -