Tabu-driven Quantum Neighborhood Samplers

• URL: http://arxiv.org/abs/2011.09508v2
• Date: Thu, 4 Mar 2021 08:55:41 GMT
• Title: Tabu-driven Quantum Neighborhood Samplers
• Authors: Charles Moussa, Hao Wang, Henri Calandra, Thomas B\"ack, Vedran Dunjko
• Abstract summary: Combinatorial optimization is an important application numerically targeted by quantum computing. One option to achieve advantages with near-term devices is to use them in combination with classicals. We show that QAOA provides a flexible tool for exploration Algorithm-exploitation in such hybrid settings.
• Score: 2.9934511331003555
• License: http://creativecommons.org/licenses/by/4.0/
• 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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