  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.
