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Tabu-Driven Quantum Neighborhood Samplers

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12692)

Abstract

Combinatorial optimization is an important application targeted by quantum computing. However, near-term hardware constraints make quantum algorithms unlikely to be competitive when compared to high-performing classical heuristics on large practical problems. One option to achieve advantages with near-term devices is to use them in combination with classical heuristics. In particular, we propose using quantum methods to sample from classically intractable distributions – which is the most probable approach to attain a true provable quantum separation in the near-term – which are used to solve optimization problems faster. We numerically study this enhancement by an adaptation of Tabu Search using the Quantum Approximate Optimization Algorithm (QAOA) as a neighborhood sampler. We show that QAOA provides a flexible tool for exploration-exploitation in such hybrid settings and can provide evidence that it can help in solving problems faster by saving many tabu iterations and achieving better solutions.

Keywords

  • Quantum computing
  • Combinatorial optimization
  • Tabu search

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Notes

  1. 1.

    When using BIPOP-CMAES, we run circuits with 1000 measurements to estimate expectation values. The optimizer stops when it has reached 2000 evaluations. We obtained great performances in terms of averaged ratios (as the evaluations divided by the optimum of the subproblem), superior to 0.97 at the considered depths.

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Acknowledgements

CM, TB and VD acknowledge support from Total. This work was supported by the Dutch Research Council (NWO/OCW), as part of the Quantum Software Consortium programme (project number 024.003.037). This research is also supported by the project NEASQC funded from the European Union’s Horizon 2020 research and innovation programme (grant agreement No 951821).

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Moussa, C., Wang, H., Calandra, H., Bäck, T., Dunjko, V. (2021). Tabu-Driven Quantum Neighborhood Samplers. In: Zarges, C., Verel, S. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2021. Lecture Notes in Computer Science(), vol 12692. Springer, Cham. https://doi.org/10.1007/978-3-030-72904-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-72904-2_7

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