将 Q-score 扩展为应用级量子度量框架
Extending the Q-score to an Application-level Quantum Metric Framework
AI总结：
本文扩展 Atos 的 Q-score 为通用量子度量框架，并新增 Q-score Max-Clique，首次实现应用级原生比较量子退火、门基和光子三种量子计算范式，并与经典求解器对比。
摘要：
评估量子设备的性能是扩展量子设备规模并最终在实际中应用它们的重要一步。大量可用的量子度量以及用于开发量子计算机的不同硬件技术使这种评估变得复杂。此外，不同的计算范式以不同方式实现量子操作。一个著名的量子度量是 Atos 的 Q-score 度量。该度量最初是作为使用 Max-Cut 问题对设备进行基准测试的独立方法引入的。在这项工作中，我们表明 Q-score 定义了一个量子度量框架，该框架允许使用不同的问题、用户设置和求解器进行基准测试。为了展示该框架的适用性，我们在此框架中展示了第二个 Q-score，称为 Q-score Max-Clique。据我们所知，这产生了第一个能够原生比较三种不同量子计算范式的应用级度量。该度量在这些计算量子范式——量子退火、基于门的量子计算和光子量子计算——上进行了评估，并将结果与经典求解器获得的结果进行了比较。
英文摘要：
Evaluating the performance of quantum devices is an important step towards scaling quantum devices and eventually using them in practice. The great number of available quantum metrics and the different hardware technologies used to develop quantum computers complicate this evaluation. In addition, different computational paradigms implement quantum operations in different ways. A prominent quantum metric is given by the Q-score metric of Atos. This metric was originally introduced as a standalone way to benchmark devices using the Max-Cut problem. In this work, we show that the Q-score defines a framework of quantum metrics, which allows benchmarking using different problems, user settings and solvers. To showcase the applicability of the framework, we showcase a second Q-score in this framework, called the Q-score Max-Clique. This yields, to our knowledge, the first application-level metric capable of natively comparing three different paradigms of quantum computing. This metric is evaluated on these computational quantum paradigms -- quantum annealing, gate-based quantum computing, and photonic quantum computing -- and the results are compared to those obtained by classical solvers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
