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arXiv · 2011.09508

Tabu-driven Quantum Neighborhood Samplers

Charles Moussa·Hao Wang·Henri Calandra·Thomas Bäck·Vedran Dunjko
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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.

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Charles Moussa, Hao Wang, Henri Calandra, Thomas Bäck, Vedran Dunjko. 2021-03-04. Tabu-driven Quantum Neighborhood Samplers. https://doi.org/10.1007/978-3-030-72904-2_7

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