The quantum convolutional neural network (QCNN) is a promising quantum machine learning (QML) model that is expected to achieve quantum advantages in classically intractable problems. However, the QCNN requires a large number of measurements for data learning, limiting its practical applications in large-scale problems. To alleviate this requirement, we propose a novel architecture called split-parallelizing QCNN (sp-QCNN), which exploits the prior knowledge of quantum data to design an efficient model. This architecture draws inspiration from geometric quantum machine learning and targets translationally symmetric quantum data commonly encountered in physics and quantum computing science. By splitting the quantum circuit based on translational symmetry, the sp-QCNN can substantially parallelize the conventional QCNN without increasing the number of qubits and improve the measurement efficiency by an order of the number of qubits. To demonstrate its effectiveness, we apply the sp-QCNN to a quantum phase recognition task and show that it can achieve comparable classification accuracy to the conventional QCNN while considerably reducing the measurement resources required. Due to its high measurement efficiency, the sp-QCNN can mitigate statistical errors in estimating the gradient of the loss function, thereby accelerating the learning process. These results open up new possibilities for incorporating the prior data knowledge into the efficient design of QML models, leading to practical quantum advantages.

翻译：量子卷积神经网络（QCNN）是一种有前景的量子机器学习（QML）模型，有望在经典难以处理的问题中实现量子优势。然而，QCNN在数据学习中需要大量测量，这限制了其在大型问题中的实际应用。为缓解这一需求，我们提出了一种称为分裂并行化QCNN（sp-QCNN）的新型架构，该架构利用量子数据的先验知识来设计高效模型。该架构受几何量子机器学习的启发，针对物理和量子计算科学中常见的平移对称量子数据。通过基于平移对称性分裂量子电路，sp-QCNN可以在不增加量子比特数量的前提下大幅并行化传统QCNN，并将测量效率提升一个量子比特数量级。为验证其有效性，我们将sp-QCNN应用于量子相位识别任务，结果表明，在显著减少测量资源的同时，其分类精度可与传统QCNN相媲美。由于高测量效率，sp-QCNN能够减少估计损失函数梯度时的统计误差，从而加速学习过程。这些结果揭示了将数据先验知识融入QML模型高效设计的新可能性，进而推动实际量子优势的实现。