Benchmarking Quantum Process Units (QPU) at an application level usually requires considering the whole programming stack of the quantum computer. One critical task is the minor-embedding (resp. transpilation) step, which involves space-time overheads for annealing-based (resp. gate-based) quantum computers. This paper establishes a new protocol to generate graph instances with their associated near-optimal minor-embedding mappings to D-Wave Quantum Annealers (QA). This set of favorable mappings is used to generate a wide diversity of optimization problem instances. We use this method to benchmark QA on large instances of unconstrained and constrained optimization problems and compare the performance of the QPU with efficient classical solvers. The benchmark aims to evaluate and quantify the key characteristics of instances that could benefit from the use of a quantum computer. In this context, existing QA seem best suited for unconstrained problems on instances with densities less than $10\%$. For constrained problems, the penalty terms used to encode the hard constraints restrict the performance of QA and suggest that these QPU will be less efficient on these problems of comparable size.

翻译：在应用层面基准测试量子处理单元（QPU）通常需考虑量子计算机的整个编程栈。其中一项关键任务是小型嵌入（或转译）步骤，这会给基于退火（或基于门）的量子计算机带来时空开销。本文建立了一种新协议，用于生成图实例及其与D-Wave量子退火器（QA）相关的近最优小嵌入映射。利用这组有利的映射，我们生成了多样化的优化问题实例。我们采用该方法在无约束和约束优化问题的大规模实例上对QA进行基准测试，并将QPU的性能与高效经典求解器进行比较。该基准测试旨在评估和量化可能从量子计算机使用中获益的实例的关键特征。在此背景下，现有QA似乎最适合密度低于10%的无约束问题实例。对于约束问题，用于编码硬约束的惩罚项限制了QA的性能，表明这些QPU在同等规模的此类问题上效率较低。