用经典量子迁移学习让量子退火加速优化求解
Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer
- 先经典训练参数，再用于量子退火求解器
- 相比原方法收敛更快，执行时间减少30%以上
- 适合想提升量子优化效率的研究者
量子退火（QA）作为采样器和组合优化问题（COP）求解器受到广泛关注。近期提出的基于采样的QA求解器显著减少了所需量子比特数，可处理大规模COP。与此相关，一种基于深度学习的可训练采样求解器通过深度展开技术从数据集中优化内部参数。尽管参数学习能加快收敛速度，但受限于训练成本，该求解器仍使用经典采样器。本文提出经典-量子迁移学习：在经典环境下训练参数，再将其应用于含量子退火的求解器中。数值实验表明，采用该迁移学习策略的可训练量子COP求解器，在收敛速度和执行时间上均优于原始求解器。
原文摘要 · Abstract (English)
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.
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