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January 1, 2021IEEE Transactions on Quantum Engineering149 citationsOpen Access

Formulating and Solving Routing Problems on Quantum Computers

SHStuart M. HarwoodCGClaudio GambellaDTDimitar Trenev

Key Points

  • The aim is to explore the use of quantum computing in solving vehicle routing problems under various constraints.
  • Proposed mathematical formulations for inventory routing as a vehicle routing problem with time windows.
  • Comparison of optimization models based on their solvability on quantum devices.
  • Evaluation of algorithms using simulated quantum devices to assess benefits and robustness.
  • Simulated quantum devices demonstrated relative advantages of different quantum algorithms for routing problems.
  • The proposed formulations showed strengths and weaknesses specific to quantum computational methods.
  • Metrics indicated differences in the difficulty of solving the underlying optimization problems.

Abstract

The determination of vehicle routes fulfilling connectivity, time, and operational constraints is a well-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.

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Cite This Study

Harwood et al. (2021) studied this question.

synapsesocial.com/papers/69dff6edbdd89ea5318607d7https://doi.org/10.1109/tqe.2021.3049230
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Quantum Heuristic Approach to Vehicle Routing Problem2026
  2. 2Improving Quantum and Classical Decomposition Methods for Vehicle Routing2024 · 3 citations
  3. 3Qubit Efficient Quantum Algorithms for the Vehicle Routing Problem on Noisy Intermediate‐Scale Quantum Processors2024 · 8 citations
  4. 4A Greedy Quantum Route-Generation Algorithm2024
  5. 5Solving a Real-World Package Delivery Routing Problem Using Quantum Annealers2024