标题： 基于量子退火器的深度展开组合优化求解器的迁移学习 显示英文标题

标题： Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer

摘要： 量子退火（QA）因其作为采样器和组合优化问题（COP）求解器而受到研究兴趣。 一种最近提出的基于采样的QA求解器显著减少了所需的量子位数，能够处理大规模的COP。 在这方面，提出了一种可训练的基于采样的COP求解器，该求解器通过使用一种称为深度展开的深度学习技术，从数据集中优化其内部参数。 尽管学习内部参数可以加速收敛速度，但由于训练成本，可训练求解器中的采样器仅限于使用经典采样器。 在本研究中，为了在可训练求解器中利用QA，我们提出了经典-量子迁移学习，其中参数在经典上进行训练，并将训练后的参数用于带有QA的求解器中。 数值实验的结果表明，使用经典-量子迁移学习的可训练量子COP求解器在收敛速度和执行时间方面优于原始求解器。 显示英文摘要

摘要： Quantum annealing (QA) has attracted research interest as a sampler and combinatorial optimization problem (COP) solver. A recently proposed sampling-based solver for QA significantly reduces the required number of qubits, being capable of large COPs. In relation to this, a trainable sampling-based COP solver has been proposed that optimizes its internal parameters from a dataset by using a deep learning technique called deep unfolding. Although learning the internal parameters accelerates the convergence speed, the sampler in the trainable solver is restricted to using a classical sampler owing to the training cost. In this study, to utilize QA in the trainable solver, we propose classical-quantum transfer learning, where parameters are trained classically, and the trained parameters are used in the solver with QA. The results of numerical experiments demonstrate that the trainable quantum COP solver using classical-quantum transfer learning improves convergence speed and execution time over the original solver.