# An Overview of Quantum Machine Learning Research in China
## 3 Algorithms and Applications
### Algorithms  
Algorithms are the core component of machine learning (in the classical context), and self-evidently they are also crucial for QML research. For the QML studies in China, various types of algorithms have been established and employed. Most of these quantum algorithms stem from their classical counterparts, e.g., QPCA is inspired by classical PCA [39]; and quantum partial least squares (QPLS) is a quantum version of the classical partial least squares (PLS) [40,41]. The quantum algorithms extensively adopted in China's QML studies include quantum neural network (QNN)-type algorithms [34, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64], quantum support vector machine (QSVM) algorithms [65, 66, 67, 68], quantum K-nearest neighbor (QKNN) algorithms [36, 69, 70, 71], and so on. Among them, the QNN-type algorithms dominate in terms of the number of papers, mainly consisting of regular QNN [42, 43, 44, 45, 46, 47, 48, 49, 50], quantum convolutional neural network (QCNN) [34, 51, 52, 53, 54, 55, 56, 57], quantum deep neural network (QDNN) [58, 59, 60], quantum generative adversarial network (QGAN) [61, 62, 63, 64], etc. For a typical QNN algorithm, there are three main investigation aspects, which will be elucidated below by taking QCNN as a representative example. The first aspect is the pattern of coordination with classical computation methodology. In order to realize efficient coordination between quantum computers and classical computers, most researchers adopt hybrid quantum-classical network architectures [34, 51, 52, 54, 55], while there are also a small number of studies in which the network has a principal structure of pure variational quantum circuits and only collaborates with classical computers in the parameter optimization part [57]. The second aspect is the approach to realize the quantization of network architecture. In some studies, researchers have developed efficient quantum convolutional layers that can significantly decrease the computational complexity and, hence, reduce the computational resources [51, 53, 54]; [PERSON] et al. have not only designed quantum convolutional layers in PQC but also combined classical fully connected layers with PQC to develop hybrid quantum-classical fully connected layers [52]; [PERSON] et al. have developed a fully quantum version of the classical CNN structure that including convolutional layers, pooling layers, and fully connected layers [55]. The third aspect is the quantum state encoding method. Some studies have employed amplitude encoding [51, 53], some studies have employed angle encoding [52], and [PERSON] et al. have developed a tree-structured hybrid amplitude encoding scheme by integrating the advantages of amplitude encoding and angle encoding, hence providing flexibility and stability in adjusting the width and the depth of the quantum circuit [57]. In addition to the hotspot algorithms like QNN, a few studies utilizing less popular algorithms have also achieved good results. [PERSON] et al. improved a quantum support matrix machine (QSMM) algorithm by leveraging a quantum matrix inversion (QMI) technique. Compared to a former version of QSMM, the dependence on the precision of the upgraded QSMM is exponentially improved, showing strong potential for applications in image classification [72]. [PERSON] et al. proposed a linear-layer-enhanced quantum long short-term memory (QLSTM) model, which adopts the linear layers before and after the variational quantum circuit (VQC) of QLSTM to extract features. This model can effectively reduce the number of qubits required while amplifying quantum advantages, showing promising prospects for price forecast applications [73].