论文标题
np-hard，但不再难以解决？使用量子计算来解决优化问题
NP-hard but no longer hard to solve? Using quantum computing to tackle optimization problems
论文作者
论文摘要
在过去的十年中，公共和工业研究资金通过实验将量子计算从Shor算法的早期承诺转移到嘈杂的中级量表量子设备（NISQ）的时代，以解决现实世界中的问题。量子方法可能可以有效地解决经典方法失败的某些（NP-）硬优化问题。从我们的角度来看，我们检查了使用量子计算机解决优化问题的量子优化领域。我们通过适当的用例来证明这一点，并讨论量子计算机的当前质量，其求解器功能以及基准测试困难。尽管我们显示了概念验证的证明，而不是完整的基准测试，但我们使用结果来强调在比较量子和经典方法时使用适当指标的重要性。我们在讨论一些量子优化的突破以及当前状态和未来方向的讨论中结束。
In the last decade, public and industrial research funding has moved quantum computing from the early promises of Shor's algorithm through experiments to the era of noisy intermediate scale quantum devices (NISQ) for solving real-world problems. It is likely that quantum methods can efficiently solve certain (NP-)hard optimization problems where classical approaches fail. In our perspective, we examine the field of quantum optimization where we solve optimisation problems using quantum computers. We demonstrate this through a proper use case and discuss the current quality of quantum computers, their solver capabilities, and benchmarking difficulties. Although we show a proof-of-concept rather than a full benchmark, we use the results to emphasize the importance of using appropriate metrics when comparing quantum and classical methods. We conclude with discussion on some recent quantum optimization breakthroughs and the current status and future directions.
