Abstract Quantum neural networks (QNNs) have made significant progress in theoretical research, yet their application to real‐world tasks remains highly challenging. To handle complex signal classification tasks and reduce the impact of decoherence effects on real quantum devices, it is necessary to study a QNN that both supports multi‐dimensional input features and has small‐scale quantum circuits. Based on the theoretical derivation of the unitary invariance of matrix operations, this paper proposes a novel multi‐block quantum neural network(MQNN), which supports direct rotational encoding of multi‐dimensional features. MQNN combines multiple small‐scale quantum circuits to replace the quantum operations of large quantum circuits, and collaboratively processes multi‐dimensional features to complete the classification task. MQNN is applied to the real collected dataset of radar echo signals from unmanned aerial vehicles(UAVs). The 64‐dimensional Doppler spectrum features extracted from the dataset can be simultaneously rotationally encoded into 64 qubits of the MQNN. Compared with five quantum machine learning algorithms (QSVM, QKNN, QCNN, QNNC, and QNNS), MQNN has the highest accuracy. Moreover, MQNN also has a smaller quantum circuit scale than the amplitude encoded quantum methods, thus it has a stronger anti‐decoherence ability. MQNN provides a useful reference for other quantum algorithms in processing multi‐dimensional data.
Wang et al. (Sun,) studied this question.