The Quantum approximate optimization algorithm (QAOA) constitutes one of the often mentioned candidates expected to yield a quantum boost in the era of near-term quantum computing. In practice, quantum optimization will have to compete with cheaper classical heuristic methods, which have the advantage of decades of empirical domain-specific enhancements. Consequently, to achieve optimal performance we will face the issue of algorithm selection, well-studied in practical computing. Here we introduce this problem to the quantum optimization domain. Specifically, we study the problem of detecting those problem instances of where QAOA is most likely to yield an advantage over a conventional algorithm. As our case study, we compare QAOA against the well-understood approximation algorithm of Goemans and Williamson on the Max-Cut problem. As exactly predicting the performance of algorithms can be intractable, we utilize machine learning (ML) to identify when to resort to the quantum al...

中文翻译：

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量子化或非量子化：朝着近期量子优化中的算法选择

量子近似优化算法（QAOA）构成了人们经常提到的候选方法之一，这些候选方法有望在近期量子计算时代产生量子提升。在实践中，量子优化将不得不与更便宜的经典启发式方法竞争，后者具有数十年经验性领域特定增强的优势。因此，要获得最佳性能，我们将面临在实际计算中经过充分研究的算法选择问题。在这里，我们将这个问题介绍给量子优化领域。具体而言，我们研究了检测那些QAOA最有可能比传统算法产生优势的问题实例的问题。作为案例研究，我们将QAOA与Goemans和Williamson在Max-Cut问题上广为人知的近似算法进行了比较。