Machine learning with hierarchical quantum circuits, usually referred to as Quantum Convolutional Neural Networks (QCNNs), is a promising prospect for near-term quantum computing. The QCNN is a circuit model inspired by the architecture of Convolutional Neural Networks (CNNs). CNNs are successful because they do not need manual feature design and can learn high-level features from raw data. Neural Architecture Search (NAS) builds on this success by learning network architecture and achieves state-of-the-art performance. However, applying NAS to QCNNs presents unique challenges due to the lack of a well-defined search space. In this work, we propose a novel framework for representing QCNN architectures using techniques from NAS, which enables search space design and architecture search. Using this framework, we generate a family of popular QCNNs, those resembling reverse binary trees. We then evaluate this family of models on a music genre classification dataset, GTZAN, to justify the importance of circuit architecture. Furthermore, we employ a genetic algorithm to perform Quantum Phase Recognition (QPR) as an example of architecture search with our representation. This work provides a way to improve model performance without increasing complexity and to jump around the cost landscape to avoid barren plateaus. Finally, we implement the framework as an open-source Python package to enable dynamic QCNN creation and facilitate QCNN search space design for NAS.

翻译：分层量子电路的机器学习，通常被称为量子卷积神经网络（QCNNs），是近期量子计算中一个有前景的方向。QCNN是一种受卷积神经网络（CNNs）架构启发的电路模型。CNNs之所以成功，是因为它们无需手动特征设计，能够从原始数据中学习高层特征。神经架构搜索（NAS）在此基础上通过学习网络架构取得了最新性能水平。然而，将NAS应用于QCNNs面临独特挑战，因为缺乏明确定义的搜索空间。本文提出一个新颖框架，利用NAS技术表示QCNN架构，从而支持搜索空间设计和架构搜索。通过该框架，我们生成了一族流行的QCNN——类似于逆二叉树结构的模型。随后，在音乐风格分类数据集GTZAN上评估该模型族，以验证电路架构的重要性。此外，我们采用遗传算法进行量子相位识别（QPR），作为利用所提出表示进行架构搜索的示例。本工作提供了一种在不增加复杂度的情况下提升模型性能的方法，并可在成本景观中跳跃，避免贫瘠高原。最后，我们以开源Python包形式实现该框架，实现动态QCNN创建，并促进面向NAS的QCNN搜索空间设计。