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

One of the most promising suggested applications of quantum computing is
solving classically intractable chemistry problems. This may help to answer
unresolved questions about phenomena like: high temperature superconductivity,
solid-state physics, transition metal catalysis, or certain biochemical
reactions. In turn, this increased understanding may help us to refine, and
perhaps even one day design, new compounds of scientific and industrial
importance. However, building a sufficiently large quantum computer will be a
difficult scientific challenge. As a result, developments that enable these
problems to be tackled with fewer quantum resources should be considered very
important. Driven by this potential utility, quantum computational chemistry is
rapidly emerging as an interdisciplinary field requiring knowledge of both
quantum computing and computational chemistry. This review provides a
comprehensive introduction to both computational chemistry and quantum
computing, bridging the current knowledge gap. We review the major developments
in this area, with a particular focus on near-term quantum computation.
Illustrations of key methods are provided, explicitly demonstrating how to map
chemical problems onto a quantum computer, and solve them. We conclude with an
outlook for this nascent field.

量子计算对于解决经典化学难题具有巨大潜力，但大型量子计算机的建设依然具有挑战性，因此需要更少的量子资源来解决这些问题。基于此潜在的实用价值，量子计算化学正在快速崛起，这篇综述介绍了量子计算和计算化学的基础知识，并重点介绍了近期量子计算化学的主要进展及其关键方法，并探讨了该领域的未来。