An Overview of Quantum Machine Learning Research in China
, Weiming Xu 1,2,3,*,†, Xuesen Xu 2, Jianyu Wang 1,2,3 and Rong Shu 1,2,3,4,*,‡
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsI read the article thoroughly and found that it provides a overview of the state of research on Quantum Machine Learning (QML) in China. The article is structured as follows:
1. Introduction
This section offers a basic introduction to the topic, starting from the origins of quantum information processing to today’s QML. I appreciate that the authors have referenced the standard works by Schuld, Chen, Biamonte, and Zeguendry.
2. QML Development History and Current Status in China
This section serves as a related work overview, detailing the field's growth, its current status, and expected future trends.
3. Algorithms and Applications
This section discusses the algorithms and applications found in the literature. The included table effectively summarizes the featured algorithms and their corresponding publications. Additionally, the graphic illustrating the field of applications is clear and well-designed.
4. Innovation Aspect Analysis
The authors categorize the innovation aspects into three subsections: algorithm/model design, strategy selection, and application scenarios. This separation is logical and is supported by numerous references.
5. Challenges and Prospects
This is an informative section that appropriately discusses international aspects of the challenges faced in the field, which adds valuable context.
6. Conclusions
The article concludes with a summary of its key points.
I believe the article is well-written and well-structured, and I recommend it for publication.
As a minor stylistic suggestion, I think each section should begin with a short introductory paragraph before immediately jumping into a subsection. For example, instead of:
2. QML Development History and Current Status in China
2.1. Development History
Consider restructuring it as:
2. QML Development History and Current Status in China
In the following, we discuss the historical development of QML with a strong focus on its progress in China.
2.1. Development History
Making this change for all sections would improve the article's readability and visual appeal. While this is not a strict requirement, I believe it would enhance the overall presentation.
Additionally, I suggest the authors briefly address that QML sometimes struggles with poor performance on basic machine learning problems and can be somewhat overhyped. I recommend integrating the following publication into Section 5.2.2 General Problems (as it already includes references beyond Chinese research, such as Google’s work):
Raubitzek, S., & Mallinger, K. (2023). On the Applicability of Quantum Machine Learning. Entropy, 25(7), 992. https://doi.org/10.3390/e25070992
This could be included in one or two sentences. My main point is that while QML is an exciting and growing research field—China’s contributions are particularly noteworthy, as the article rightly highlights—there are also challenges related to performance and fair comparisons in the field, as discussed in the referenced paper.
Finally, I noticed that Figure 1 has a grey background. Please consider removing it and using a transparent or white background instead for better consistency and clarity.
I particularly appreciate the article’s focus on and clear differentiation between the various QML algorithms.
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsReviewer Report
Manuscript Title: "An Overview of Quantum Machine Learning Research in China"
The authors present a comprehensive overview of the state of the art in quantum machine learning (QML) research in China, which is an important and timely topic. While the approach proposed by the authors is interesting—organizing China's developments in quantum machine learning into three stages—the results appear oversimplified and suggest the need for a more thorough and careful examination of the actual state of QML research in China. I have outlined my concerns and suggestions below to guide the authors in improving their manuscript.
Major Comments
Lack of Source References for Key Claims
In Fig. 2, the authors categorize the development of QML in China into three stages, stating that fewer than 10 papers were published before 2019, 20–30 publications appeared between 2019 and 2021, and over 60 publications have been published since 2022. However, the source of these claims is not provided.
Suggestion: Could the authors elaborate on the methodology used to derive these numbers? Please provide detailed references or sources to substantiate these claims.
Recommendation to Use Advanced Data Platforms
I recommend the authors leverage established data platforms to enhance the accuracy and reliability of their findings. For instance, a preliminary search on Dimensions.ai using the keywords "National Natural Science Foundation of China (NSFC)" and "quantum machine learning" yielded the following results as of February 13, 2025:
Publications: 4,624,576
Datasets: 465,459
Grants: 561,771
Funding Amount: USD 39.11 billion
Clinical Trials (linked): 5,728
Suggestion: To improve the rigor of the study, I encourage the authors to utilize platforms such as Dimensions.ai, Scopus, arXiv, and CNKI (China National Knowledge Infrastructure) to filter, analyze, and report data for the three stages proposed.
Contrast Between Platforms: It would be highly valuable to contrast results obtained from different platforms, such as Dimensions.ai and CNKI, to highlight possible discrepancies or trends in publication data.
Use of Python for Data Analysis: The authors are encouraged to consider automating their data collection and analysis using Python packages, which could enhance the accuracy and reproducibility of their results. Suggested tools include:
requests: For HTTP requests to online databases.
BeautifulSoup: For parsing and scraping HTML from sources like Google Scholar or CNKI.
selenium: For automating searches on interactive platforms.
scholarly: For programmatically accessing Google Scholar.
arxiv: For retrieving data from arXiv.
serpapi: For search engine APIs like Google Scholar.
Suggestion: Employing such tools could enable the authors to present more precise and quantitative information about the growth of QML research in China.
Algorithms and Applications: The discussion of algorithms and applications could be significantly refined by leveraging the aforementioned data analysis tools. This would help the authors provide a more accurate and in-depth perspective on the advancements in QML.
Highlighting Exponential Growth Topics: It would be valuable to identify and emphasize specific QML topics that have demonstrated exponential growth over time. This analysis would add depth and relevance to the manuscript.
Minor Comments
M1. On line 57, please ensure the correct LaTeX notation is used for quantum states.
M2. Figure 1 requires further explanation in the main text. For example, the authors reference Bell states but do not provide sufficient context or discussion about them.
M3. I suggest including a short section on classical machine learning to clarify which aspects of quantum physics can be incorporated at various stages of input processing.
Overall Recommendation: In conclusion, while the topic of this manuscript is highly relevant and the proposed approach has potential, the current version requires major revisions to improve the accuracy, clarity, and depth of the analysis. Based on the aforementioned reasons, I regret to inform the authors that I cannot recommend the manuscript for publication in its current form. I encourage the authors to address these issues comprehensively and submit a revised version.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for AuthorsThis article provides a comprehensive review of the QML-research in China. The QML development in China is presented in terms of research ideas and tasks, and the algorithms and application fields are sorted out. We have also highlighted some typical creative studies and illuminated their innovation points. Furthermore, the current challenges and future prospects are discussed. This review may provide inspiration for both China’s and global QML-domain progress. Their research review is very interesting and the presentation is very good. It would be better if the authors could consider the following comments:
- In figure 1, pp 2, the Bell states are not included in the test. Please include them in the text.
- Please specify the |Φ(+-)> and |Ψ(+-)>.
- Although the authors have mentioned the quantum computing research area in their manuscript, they do not mention any physical quantum structure to achieve it. Could they mention any physical nanostructure like quantum dots which have been used as “charge-qubit, spin-qubit ” (e.g. Solid state communications 191, 10-13 (2014), Physical Review B 80 (15), 153308 (2009) or any other article they prefer)?
- Could they use any applicable model to describe the “noise” in the circuits?”
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Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
Comments and Suggestions for AuthorsI express my gratitude to the authors for their comprehensive responses addressing my comments. I am pleased to note that I am satisfied with the majority of their explanations and clarifications regarding my inquiries. Of particular importance is their diligent execution of additional analysis, including numerical assessments, as well as the substantial revision of previously undefined parameters.
As a direct outcome of these efforts, the revised manuscript has evolved into a highly intriguing piece of work, characterized by its well writing and clear presentation. I am pleased to state that no significant general or physical objections remain in relation to its content. Therefore, I am more than delighted to recommend its publication.
