Quantum convolutional neural networks (QCNNs) represent a promising approach
in quantum machine learning, paving new directions for both quantum and
classical data analysis. This approach is particularly attractive due to the
absence of the barren plateau problem, a fundamental challenge in training
quantum neural networks (QNNs), and its feasibility. However, a limitation
arises when applying QCNNs to classical data. The network architecture is most
natural when the number of input qubits is a power of two, as this number is
reduced by a factor of two in each pooling layer. The number of input qubits
determines the dimensions (i.e. the number of features) of the input data that
can be processed, restricting the applicability of QCNN algorithms to
real-world data. To address this issue, we propose a QCNN architecture capable
of handling arbitrary input data dimensions while optimizing the allocation of
quantum resources such as ancillary qubits and quantum gates. This optimization
is not only important for minimizing computational resources, but also
essential in noisy intermediate-scale quantum (NISQ) computing, as the size of
the quantum circuits that can be executed reliably is limited. Through
numerical simulations, we benchmarked the classification performance of various
QCNN architectures when handling arbitrary input data dimensions on the MNIST
and Breast Cancer datasets. The results validate that the proposed QCNN
architecture achieves excellent classification performance while utilizing a
minimal resource overhead, providing an optimal solution when reliable quantum
computation is constrained by noise and imperfections.

该论文介绍了一个能够处理任意输入数据维度的量子卷积神经网络 (QCNN) 架构，优化了量子资源的分配，达到了优秀的分类性能，同时最小化了资源开销，为噪声和不完美情况下可靠量子计算提供了最佳解决方案。

针对任意数据维度优化量子卷积神经网络结构

Optimizing Quantum Convolutional Neural Network Architectures for  Arbitrary Data Dimension

The Quantum Convolutional Neural Network (QCNN) is a quantum 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, NAS requires the design of a search space, which is
currently not possible for QCNNs as no formal framework exists to capture its
design elements. In this work, we provide such a framework by using techniques
from NAS to create a hierarchical representation for QCNN architectures. 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, showing that alternating architecture has a
greater impact on model performance than other modelling components, such as
the choice of unitary ansatz and data encoding. Our framework 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.

本文提供了一种 Quantum Convolutional Neural Network (QCNN) 体系结构设计的新方法，使用 Neural Architecture Search (NAS) 技术生成一系列常用的 QCNN 体系结构，并在音乐类型分类数据集上进行了评估，为 NAS 提供了 QC﻿﻿NN 搜索空间设计的框架，最后实现该框架作为开源的 Python 包。