论文标题
NISQ算法何时开始在离散制造中创建价值？
When could NISQ algorithms start to create value in discrete manufacturing ?
论文作者
论文摘要
在短期内可以实现离散制造的量子优势吗？作为与制造业相关的NISQ算法，我们确定了用于组合优化的量子退火（QA）和量子近似优化算法（QAOA），以及用于求解非线性PDE的衍生量子电路（DQC）。虽然有证据表明QAOA的表现要出色，但这需要NISQ后电路深度。对于质量保证，与经典计算相比，现在没有毫无疑问的优势证据。然而，不同的协议可能会导致查找此类实例。与精心挑选的量子特征图一起，DQC是一个有前途的概念。随后可能会针对更高维度问题和培训改善进行进一步调查。
Are quantum advantages in discrete manufacturing achievable in the near term? As manufacturing-relevant NISQ algorithms, we identified Quantum Annealing (QA) and the Quantum Approximate Optimization Algorithm (QAOA) for combinatorial optimization as well as Derivative Quantum Circuits (DQC) for solving non-linear PDEs. While there is evidence for QAOA's outperformance, this requires post-NISQ circuit depths. In the case of QA, there is up to now no unquestionable evidence for advantage compared to classical computation. Yet different protocols could lead to finding such instances. Together with a well-chosen quantum feature map, DQC are a promising concept. Further investigations for higher dimensional problems and improvements in training could follow.
