论文标题
量子机器学习的示意图设计和研究
Diagrammatic Design and Study of Ansätze for Quantum Machine Learning
论文作者
论文摘要
鉴于量子机学习（QML）的普及程度不断提高，很重要的是开发有效简化常见的参数化量子电路（通常称为Ansätze）的家族。该论文开创了使用示意技术用qmlAnsätze推理的。我们采用常用的QMLAnsätze并将其转换为图表形式，并完整描述了这些大门的通勤方式，从而使电路更容易分析和简化。此外，我们利用了CNOT和相位小工具之间相互作用的组合描述，以分析分层Ansätze中的周期性现象，并简化QML常用的一类电路。
Given the rising popularity of quantum machine learning (QML), it is important to develop techniques that effectively simplify commonly adopted families of parameterised quantum circuits (commonly known as ansätze). This thesis pioneers the use of diagrammatic techniques to reason with QML ansätze. We take commonly used QML ansätze and convert them to diagrammatic form and give a full description of how these gates commute, making the circuits much easier to analyse and simplify. Furthermore, we leverage a combinatorial description of the interaction between CNOTs and phase gadgets to analyse a periodicity phenomenon in layered ansätze and also to simplify a class of circuits commonly used in QML.
