Reservoir Computing Using Measurement-Controlled Quantum Dynamics

Round 1

Reviewer 1 Report

Below, I detail my assessment and the reasons for my recommendation.

Originality and Significance: The manuscript presents a novel approach by integrating quantum dynamics into RC, specifically through the dynamics of a probed atom in a cavity. This approach not only enriches the field of machine learning algorithms but also bridges it with quantum mechanics practically and innovatively. Using a measurement-controlled quantum evolution for forecasting is original and significant, especially considering the increasing interest in quantum computing.

Theoretical Validation: The authors have provided a solid theoretical framework to validate the operation of the proposed quantum reservoir. This is crucial for the credibility and scientific rigor of the study. The manuscript demonstrates how this quantum RC system can be beneficial in error-tolerant applications and scenarios with limited computational and energy resources. This theoretical validation strengthens the manuscript’s contribution to the field.

Comparison with Traditional RC Algorithms: The paper effectively highlights the advantages of the proposed system over traditional RC algorithms, especially in terms of the number of artificial neurons required for reliable forecasts. This comparison is essential to understand the efficiency and potential applications of the quantum RC system. However, I suggest the authors consider adding more detailed quantitative comparisons or case studies to further solidify these claims.

Clarity and Presentation: The manuscript is well-written, and the concepts are presented. The abstract provides a succinct and accurate paper summary, setting clear expectations for the reader. The logical flow of the paper facilitates understanding, making it accessible to readers who may not be specialists in quantum computing.

Recommendations for Improvement:

1. Quantitative Analysis: As previously mentioned, a more detailed quantitative comparison with traditional RC algorithms could be beneficial.
2. Potential Applications: Expanding on the potential applications, especially in real-world scenarios, could enhance the practical relevance of the paper.
3. Future Work: A section on future work addressing potential challenges or limitations of the proposed system would be beneficial for guiding subsequent research in this area.

In conclusion, the manuscript substantially contributes to machine learning and quantum computing. It is well-positioned to spark further research and development in this area. I am confident that it will be of great interest to the readers of your journal.

ok

Author Response

We thank the Reviewer for their constructive and generally positive comments. The Reviewer’s suggestions help us improve the clarity and accuracy of the presentation of our results, also enabling us to make our manuscript more accessible to the broad readership of the journal. We have revised the manuscript and addressed all concerns and suggestions raised in the Reviewer’s report. In the attachment, we provide our point-to-point responses, where the Reviewer’s comments are shown in the black colour but our responses are marked by the blue colour. Thus, we resubmit the revised manuscript for your further consideration for publication in Electronics.

Sincerely,
Abbas Hussein and Ivan S. Maksymov.

Author Response File: Author Response.pdf

Reviewer 2 Report

The authors propose a novel machine learning algorithm that leverages the dynamics of a quantum system to handle highly nonlinear and chaotic phenomena. The algorithm demonstrates good performance in dealing with such phenomena compared to traditional RC algorithms. One significant advantage of the proposed quantum reservoir is its reduced requirement for artificial neurons in the prediction process. The authors theoretically validate the operation of the reservoir and showcase its potential in error-tolerant applications.

The paper provides a clear background and motivation for the research, along with a detailed description of the structure and working principles of the proposed quantum reservoir. Theoretical validation results are also presented. However, there are a few aspects that could be further improved and addressed:

1.     The manuscript is comprehensive, but it could benefit from improved organization and clarity, especially in the Introduction and conclusion sections.

2.      Provide more detailed explanations on the implementation of coherent driving and measurement-controlled quantum evolution when introducing the structure and working principles of the quantum reservoir. This will help readers better understand the specific implementation of the system.

3.      Discuss the advantages and limitations of the proposed method compared to other machine learning algorithms. It would be beneficial to include a summary table comparing the performance of recent relevant works with the proposed approach.

4.      Authors need to check the information of the references and unify the format.

Author Response

We thank the Reviewer for their constructive and generally positive comments. The Reviewer’s suggestions help us improve the clarity and accuracy of the presentation of our results, also enabling us to make our manuscript more accessible to the broad readership of the journal. We have revised the manuscript and addressed all concerns and suggestions raised in the Reviewer’s report. In the attachment, we provide our point-to-point responses, where the Reviewer’s comments are shown in the black colour but our responses are marked by the blue colour. Thus, we resubmit the revised manuscript for your further consideration for publication in Electronics.

Sincerely,
Abbas Hussein and Ivan S. Maksymov.

Author Response File: Author Response.pdf

Reviewer 3 Report

In this manuscript, the authors described a novel approach to reservoir computing (RC). Using quantum measurement rates to control the dynamics of a probed atom within a cavity. They also discussed the potential of using such quantum reservoirs to forecast chaotic phenomena with greatly reduced artificial neurons (e.g., 16 neurons) compared to traditional RC systems that may require thousands. Their theoretical validation highlights the advantages of quantum RC in computational efficiency and the ability to achieve accurate forecasts with shorter training datasets. They suggested that this approach could be particularly useful in approximation computing, embedded systems, and computational resource-limited scenarios. I believe this manuscript is well-written and suitable for publication.

Author Response

We thank the Reviewer for their constructive and positive comments. The Reviewer’s overall tone is very favourable. We have revised the manuscript to address the comments and concerns raised by the Reviewer. We therefore resubmit the revised manuscript for your further consideration for publication in Electronics.

Sincerely,
Abbas Hussein and Ivan S. Maksymov.

Author Response File: Author Response.pdf