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Quantum reservoir probing: an inverse paradigm of quantum reservoir computing for exploring quantum many-body physics
by Kaito Kobayashi, Yukitoshi Motome
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Submission summary
| Authors (as registered SciPost users): | Kaito Kobayashi |
| Submission information | |
|---|---|
| Preprint Link: | scipost_202407_00044v3 (pdf) |
| Date submitted: | March 12, 2025, 6:12 a.m. |
| Submitted by: | Kobayashi, Kaito |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational |
Abstract
Quantum reservoir computing (QRC) is a brain-inspired computational paradigm that exploits the natural dynamics of a quantum system for information processing. To date, a multitude of quantum systems have been utilized in the QRC, with diverse computational capabilities demonstrated accordingly. This study proposes a reciprocal research direction: probing quantum systems themselves through their information processing performance in the QRC framework. Building upon this concept, here we develop quantum reservoir probing (QRP), an inverse extension of the QRC. The QRP establishes an operator-level linkage between physical properties and performance in computing. A systematic scan of this correspondence reveals the intrinsic quantum dynamics of the reservoir system from computational and informational perspectives. Unifying quantum information and quantum matter, the QRP holds great promise as a potent tool for exploring various aspects of quantum many-body physics. In this study, we specifically apply it to analyze information propagation in a one-dimensional quantum Ising chain. We demonstrate that the QRP not only distinguishes between ballistic and diffusive information propagation, reflecting the system's dynamical characteristics, but also identifies system-specific information propagation channels, a distinct advantage over conventional methods.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block
Author comments upon resubmission
With this resubmission, we have included a PDF file (reply.pdf) with our detailed responses to the reviewer’s comments. Please find it in the “Reports on this Submission” section of our “SciPost Submission Page.”
Sincerely,
Kaito Kobayashi and Yukitoshi Motome
List of changes
[1] 3rd paragraph in Sec. 4: We interchanged the original third and fourth sentences to enhance readability.
[2] 3rd paragraph in Sec. 4: We introduced additional sentences to highlight the design flexibility of QRP, which facilitates a wide range of potential applications. (Note: Although our reply.pdf stated that this revision related to the second paragraph, we repositioned it for better logical flow.)
Others
[3] We corrected typographical errors throughout the manuscript and refined the phrasing for improved clarity and precision.
Current status:
Reports on this Submission
Report #2 by Anonymous (Referee 2) on 2025-4-10 (Invited Report)
- Cite as: Anonymous, Report on arXiv:scipost_202407_00044v3, delivered 2025-04-10, doi: 10.21468/SciPost.Report.10992
Strengths
- Originality of the approach
Weaknesses
-Restricted analysis of parameter space -Unexplored role of memory effects - Lack of discussion about critical points
Report
After outlining the general features and properties of QRC, the authors introduce a protocol that aims to reconstruct the value of a random input number via linear regression of dynamical observables, as usually done in reservoir computing.
The idea of the work is certainly interesting, however, there are several aspects that, in my opinion, need to be improved in order for the manuscript to be accepted for publication in SciPost Physics.
First of all, I see many qualitative observations (if the dynamics of systems in different phases are different, it is not surprising that the computational performance looks different). While the authors mention that such performance can be used as a metric, I fail to see it.
Furthermore, the protocol seems to rely on a systematic scan of different read-out operators, which is not necessarily an efficient method.
In most of the presented results, the system is employed as an extreme machine learning model—specifically, for zero-delay tasks where memory effects are irrelevant. However, the presence or absence of memory in the dynamics is a fundamental property that distinguishes ergodic and nonergodic phases in quantum systems. This critical aspect is entirely overlooked in the manuscript, despite its direct relevance to both quantum many-body physics and reservoir computing performance. A discussion of how memory effects—or their absence—manifest in the proposed protocol would significantly strengthen the work, particularly in clarifying whether the method probes purely instantaneous properties or can also capture time-dependent correlations in the quantum system.
The study focuses exclusively on two specific points in the parameter space—one associated with chaotic dynamics and the other with ballistic information propagation—both of which are well-characterized in prior literature. While this approach provides a useful starting point, it leaves several critical questions unanswered. Most notably:
Phase Boundaries and Criticality
Generality Across Parameter Space
Broader Implications for Critical Phenomena: If, as I suspect, the protocol performs well near critical points, this could open new avenues for data-driven detection of quantum phase transitions.
In summary, while the proposed inverse QRC approach is innovative and theoretically intriguing, the study in its current form requires substantial strengthening to meet the standards for publication in SciPost Physics.
Recommendation
Ask for major revision
Author: Kaito Kobayashi on 2025-04-22 [id 5394]
(in reply to Report 2 on 2025-04-10)Please find the attached response.
Attachment:
reply.pdf