对比不同量子神经网络结构，找出最优电路设计方法。
Impact of Single Rotations and Entanglement Topologies in Quantum Neural Networks
- 测试旋转层与纠缠拓扑组合，分析电路性能差异。
- 全连接纠缠拓扑在图像生成任务中表现最佳。
- 适合研究量子机器学习电路架构的学者参考。
本文分析了不同变分量子线路在量子神经网络中的性能表现，研究其随纠缠拓扑、门操作及量子机器学习任务的变化规律。实验采用两类电路：一类为旋转与纠缠层交替的结构，另一类在此基础上增加末尾旋转层。旋转层考虑单次和双次旋转序列的所有组合，对比四种纠缠拓扑：线性、环形、成对和全连接。任务包括概率分布生成、图像生成与图像分类。结果与电路的表达能力及纠缠能力相关联，以理解这些特性如何影响性能。
原文摘要 · Abstract (English)
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.
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