论文标题
量子优势是量子机学习的正确目标吗？
Is quantum advantage the right goal for quantum machine learning?
论文作者
论文摘要
机器学习经常被列为量子计算最有前途的应用之一。实际上，这是一个奇怪的选择：当今的机器学习算法在实践中众所周知，但在理论上仍然很难研究。相比之下，量子计算不提供现实尺度的实用基准，而理论是我们必须判断它是否可能与问题相关的主要工具。从这个角度来看，我们解释了为什么很难用我们当前正在使用的工具来说量子计算机在机器学习方面的实用性。我们认为，这些挑战需要就量子优势和“击败”古典机器学习的叙述是否应继续主导文献的批判性辩论，并强调了现有研究中其他观点如何为优势的重点提供重要替代方案。
Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: Today's machine learning algorithms are notoriously powerful in practice, but remain theoretically difficult to study. Quantum computing, in contrast, does not offer practical benchmarks on realistic scales, and theory is the main tool we have to judge whether it could become relevant for a problem. In this perspective we explain why it is so difficult to say something about the practical power of quantum computers for machine learning with the tools we are currently using. We argue that these challenges call for a critical debate on whether quantum advantage and the narrative of 'beating' classical machine learning should continue to dominate the literature the way it does, and highlight examples for how other perspectives in existing research provide an important alternative to the focus on advantage.
