Tabu-driven Quantum Neighborhood Samplers

TL;DR

This paper explores a hybrid approach combining quantum sampling with classical Tabu Search to enhance combinatorial optimization, demonstrating potential for faster problem-solving and better solutions using near-term quantum devices.

Contribution

It introduces a novel hybrid method integrating QAOA as a neighborhood sampler within Tabu Search, advancing quantum-classical optimization techniques.

Findings

QAOA improves exploration-exploitation balance in Tabu Search.

Hybrid approach reduces number of tabu iterations needed.

Potential for faster solutions with near-term quantum hardware.

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.