论文标题
三分之一无监督的量子分类中的测量干扰权衡取舍
Measurement disturbance tradeoffs in three-qubit unsupervised quantum classification
论文作者
论文摘要
我们考虑量子机学习方案中旨在学习量子数据的量子扰动折衷。我们在无监督的制度中研究了二进制分类任务的最简单例子。具体而言，我们研究了两个量子位的分类如何在两个未知状态之一中，会影响我们在添加第三个量子位对三个Qubit进行随后分类的能力。令人惊讶的是，我们发现了一系列策略，其中非平凡的第一分类不会影响第二分类的成功率。但是，第一和第二分类的成功率之间存在非平凡的测量干扰折衷，我们在分析上完全表征了这一折衷。
We consider measurement disturbance tradeoffs in quantum machine learning protocols which seek to learn about quantum data. We study the simplest example of a binary classification task, in the unsupervised regime. Specifically, we investigate how a classification of two qubits, that can each be in one of two unknown states, affects our ability to perform a subsequent classification on three qubits when a third is added. Surprisingly, we find a range of strategies in which a non-trivial first classification does not affect the success rate of the second classification. There is, however, a non-trivial measurement disturbance tradeoff between the success rate of the first and second classifications, and we fully characterise this tradeoff analytically.
