# An Overview of Quantum Machine Learning Research in China
## 2 QML Development History and Current Status in China
### Development History  
Due to the lack of a clear division of China's QML development period, in this article, we have proposed a rough partition scheme that divides China's QML research into three stages, namely, the preliminary stage, transition stage, and explosion stage. The partition scheme is formulated based on the number of QML-related paper publications. We have conducted the searching and selection of paper publications based on the following methodology, referring to that adopted in [7]. Firstly, we choose two academic searching platforms, i.e., Web of Science (WOS) and China National Knowledge Infrastructure (CNKI), as our basic literature source. Secondly, we set the searching keywords as \"quantum machine learning\" OR \"quantum deep learning\" OR \"quantum neural network\". Thirdly, we filter the publication results according to these criteria: (1) the contributing institutions of the publication must include at least one Chinese institution; (2) the publication must be a research paper (excluding review papers, comment papers, dissertations, patents, etc. ); (3) the publication must be written in English. After the above filtering, we finally obtain 359 appropriate paper publications, with the specific number achieved via manual counting. Although the publication data source platforms in this work are WOS and CNKI, note that there are also some other good literature data platforms, such as Dimension.ai and PubMed. Meanwhile, it is worth emphasizing that there may exist discrepancies in the publication numbers between different platforms. For example, when we use the keyword \"quantum machine learning\" for literature searching, different platforms can yield quite different numbers of results: Dimension.ai yields about 498,000 results, WOS about 14,000, PubMed about 25,000, and CNKI about 1300, respectively. In fact, since these platforms may employ a fuzzy search mechanism, many results are actually not highly relevant contents. Therefore, careful manual examination and filtering are necessary for accurate counting of publication quantity. As shown in Figure 2a, the time before 2018 is regarded as the preliminary stage; at that time, the cumulative number of papers is less than 30. The period from 2018 to 2021 is defined as the transition stage; just the sum of paper numbers for 2018 and 2019 surpasses the total number throughout the preliminary stage, with the cumulative number of papers in the transition stage falling within the 90 to 100 range. The period starting from 2022 is considered as the explosion stage; within just three years since 2022, the number of papers has exceeded 240, and it still keeps increasing steadily year by year. In addition to the rough stage division, we have also counted the publication numbers in each year (from 2005 to 2024). In Figure 2b, we illustrate the growth trend of QML-related publication quantity over time. It can be found that the growth trend can be well fitted by an exponential curve (red dashed line), indicating that there is an exponential growth in China's QML-related publications. In the preliminary stage, researchers usually employ classical machine learning algorithms as a foundation and use quantum computing in specific modules that too complicated for classical computing. By such a mode of algorithm replacement, a quantum version of a classical machine learning algorithm can be created (denoted as \"quantum replacement mode\" hereinafter). Most studies in the preliminary stage were based on the quantum replacement mode and mainly focused on unsupervised machine learning tasks. For example, in 2017, [PERSON] et al. proposed a protocol for distributed secure QML, which can be applied in the field of cybersecurity [29]; [PERSON] et al. constructed two kinds of quantumfeature selection (dimension reduction) algorithms in 2018, and square level acceleration can be achieved compared to the corresponding classical feature selection algorithms [30].