We present a comprehensive case study comparing the performance of D-Waves'
quantum-classical hybrid framework, Fujitsu's quantum-inspired digital
annealer, and Gurobi's state-of-the-art classical solver in solving a transport
robot scheduling problem. This problem originates from an industrially relevant
real-world scenario. We provide three different models for our problem
following different design philosophies. In our benchmark, we focus on the
solution quality and end-to-end runtime of different model and solver
combinations. We find promising results for the digital annealer and some
opportunities for the hybrid quantum annealer in direct comparison with Gurobi.
Our study provides insights into the workflow for solving an
application-oriented optimization problem with different strategies, and can be
useful for evaluating the strengths and weaknesses of different approaches.

我们在一个与行业相关的真实场景中比较了 D-Wave 的量子 - 经典混合框架，Fujitsu 的量子启发式数字退火器和 Gurobi 的最先进的经典求解器在解决智能机器人调度问题时的性能，并通过三种不同的设计哲学为问题提供了三个不同的模型。在我们的基准测试中，我们着重关注不同模型和求解器组合的解决方案质量和端到端运行时间。我们发现了数字退火器的有希望结果，并且与 Gurobi 的直接比较中，混合量子退火器也有一些机会。我们的研究为解决应用导向的优化问题的工作流程提供了洞见，并且对于评估不同方法的优点和缺点非常有用。