Applying the Quantum Approximate Optimization Algorithm to the Tail Assignment Problem: part 1

ORAL

Abstract

Noisy intermediate-scale quantum (NISQ) devices, composed of a few tenths of qubits are now starting to become available. They have been shown to be able to perform some tasks that are hard to replicate on a supercomputer. However, these tasks have not been linked yet with the solution of useful problems. A relevant question is therefore whether NISQs devices can also solve real-world problems. In this work, we study numerically the solution of an airline optimization problem, namely the Tail-Assignment problem (TAS), on near-term quantum processors composed of up to 25 qubits, by using the quantum approximate optimization algorithm (QAOA).

*We acknowledge support from the Knut and Alice Wallenberg Foundation through the Wallenberg Center for Quantum Technology (WACQT). G.F. acknowledges support from the Swedish Research Council through the project grant QuACVA.

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Presenters

  • Pontus Vikstål

    • Wallenberg Centre for Quantum Technology, Department of Microtechnology and Nanoscience, Chalmers University of Technology
    • Chalmers Univ of Tech

Authors

  • Pontus Vikstål

    • Wallenberg Centre for Quantum Technology, Department of Microtechnology and Nanoscience, Chalmers University of Technology
    • Chalmers Univ of Tech

  • Mattias Grönkvist

    • Jeppesen
    • Jeppesen Systems AB

  • Marika Svensson

    • Jeppesen Systems AB

  • Martin Andersson

    • Jeppesen
    • Jeppesen Systems AB

  • Göran Johansson

    • Chalmers Univ of Tech
    • Wallenberg Centre for Quantum Technology, Department of Microtechnology and Nanoscience, Chalmers University of Technology

  • Giulia Ferrini

    • Chalmers Univ of Tech
    • Department of Microtechnology and Nanoscience, Chalmers University of Technology
    • Wallenberg Centre for Quantum Technology, Department of Microtechnology and Nanoscience, Chalmers University of Technology