Title: Impact of Single Rotations and Entanglement Topologies in Quantum Neural Networks Show Chinese title

Title: 单次旋转和纠缠拓扑结构在量子神经网络中的影响

Abstract: In this work, an analysis of the performance of different Variational Quantum Circuits is presented, investigating how it changes with respect to entanglement topology, adopted gates, and Quantum Machine Learning tasks to be performed. The objective of the analysis is to identify the optimal way to construct circuits for Quantum Neural Networks. In the presented experiments, two types of circuits are used: one with alternating layers of rotations and entanglement, and the other, similar to the first one, but with an additional final layer of rotations. As rotation layers, all combinations of one and two rotation sequences are considered. Four different entanglement topologies are compared: linear, circular, pairwise, and full. Different tasks are considered, namely the generation of probability distributions and images, and image classification. Achieved results are correlated with the expressibility and entanglement capability of the different circuits to understand how these features affect performance. Show Chinese abstract

Abstract: 在本工作中，对不同变分量子电路的性能进行了分析，研究了其如何随纠缠拓扑、采用的门和要执行的量子机器学习任务而变化。分析的目的是确定构建量子神经网络电路的最佳方式。在所进行的实验中，使用了两种类型的电路：一种是交替层的旋转和纠缠层，另一种与第一种类似，但增加了一个最终的旋转层。作为旋转层，考虑了所有单个和双旋转序列的组合。比较了四种不同的纠缠拓扑结构：线性、环形、成对和全连接。考虑了不同的任务，即概率分布和图像的生成以及图像分类。获得的结果与不同电路的表达能力和纠缠能力相关联，以了解这些特性如何影响性能。