# An Overview of Quantum Machine Learning Research in China
## 2 QML Development History and Current Status in China
### Development History
During the transition stage, researchers began to make more efforts to improve the performance of the QML algorithms already proposed. By such a mode of algorithm improvement, an upgraded version of a QML algorithm can be established (denoted as \"quantum improvement mode\" hereinafter). Despite the fact that the quantum replacement mode was still the mainstream (e.g., a quantum data compression algorithm based on PCA was proposed [31]), the number of studies based on the quantum improvement mode was gradually increasing. Meanwhile, there were more and more studies focusing on supervised machine learning tasks. In 2020, [PERSON] et al. reported two quantum locally linear embedding (QLLE) algorithms for nonlinear dimensionality reduction on the basis of a linear algebra procedure and variational hybrid quantum-classical procedure, respectively [32]. The linear algebra implementation can be considered as an example of the quantum replacement mode (replacing the classical LLE), while the variation implementation can be regarded as an example of the quantum improvement mode (improving the linear algebra implementation in terms of global manifold structure maintenance). Another representative instance of the quantum improvement mode is what [PERSON] et el. reported in 2021 as they developed a  
Figure 2: Information of China’s QML development and research papers. (**a**) A proposed three-stage division of China’s QML development period based on the number of research paper publications. (**b**) The growth trend of China’s QML publication quantity over time, indicating an approximate exponential growth. quantum deep transfer learning model able to achieve higher classification accuracy on small datasets like the popular IRIS dataset than former QML models [33]. After entering the explosion stage, the quantum improvement mode gradually becomes the predominant direction, and most studies are oriented to supervised machine learning tasks. In 2022, [PERSON] et al. proposed a variational convolutional neural network (VCNN) classification algorithm, which could achieve higher accuracy and efficiency compared with other quantum neural network (QNN) algorithms [34]. In 2023, [PERSON] et al. (the same group as that in [32]) reported another work based on the linear algebra procedure and variational hybrid quantum-classical procedure but focusing on domain adaption (DA) classification in transfer learning, and their classifier could acquire higher accuracy than classical DA classifier as well as two other state-of-art quantum DA classifiers [35]. Very recently, [PERSON] et al. developed a quantum K-nearest neighbor (QKNN) classification algorithm, which achieved higher accuracy and efficiency on the IRIS dataset than other QKNN algorithms, and the accuracy was comparable to classical KNN algorithm [36]. In short, China's QML research has undergone three stages, gradually shifting the model designing mode from the quantum replacement mode to the quantum improvement mode and meanwhile shifting the task type from unsupervised learning to supervised learning.