  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.
