SciPost Submission Page
Quantum reservoir probing: an inverse paradigm of quantum reservoir computing for exploring quantum many-body physics
by Kaito Kobayashi, Yukitoshi Motome
This is not the latest submitted version.
Submission summary
| Authors (as registered SciPost users): | Kaito Kobayashi |
| Submission information | |
|---|---|
| Preprint Link: | scipost_202407_00044v2 (pdf) |
| Date submitted: | 2024-12-28 10:20 |
| Submitted by: | Kobayashi, Kaito |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
|
| 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 attached a PDF containing our replies to the comments (reply.pdf). Please find it in the “Reports on this Submission” section of our “SciPost Submission Page.”
Sincerely,
Kaito Kobayashi and Yukitoshi Motome
List of changes
Sec. 2
[1] 1st paragraph in Sec. 2.2: We rephrased a sentence to emphasize that the washout process ensures independence from the initial state.
[2] 1st paragraph in Sec. 2.2: We added sentences to further clarify the concept of virtual time in the QRP.
[3] 1st paragraph in Sec. 2.2: We revised a previously confusing sentence (line 162 in the prior manuscript).
Sec. 3
[4] 1st paragraph in Sec. 3.1: We added a description explaining that our input protocol preserves to the intrinsic symmetry of the system.
Sec. 4
[5] 3rd paragraph in Sec. 4: We included a sentence to emphasize the potential applicability of information processing capacity and non-local observables as promising avenues for future research.
Others
[6] We resolved the inconsistency in Refs. [5, 17].
[7] We added a new reference [71].
[8] For greater clarity, we revised the wording in the manuscript as necessary.
Current status:
Reports on this Submission
Report
I appreciate the authors' sincere responses. I am satisfied with the current manuscript, but I have a few additional comments before it can be considered for publication:
・I asked in the previous round that “On the other hand, the result seems dependent on the initial state of the ancilla qubits used for input injection.”. I might be unclear and let me clarify again. My point was that the initial state with which the input-dependent state is created could be dependent on the performance. In the numerical experiments, the authors used the computational basis |00⟩ and |11⟩ to construct |ψin(sk)⟩. That is, every time step, the input is injected on the |00⟩=(I+Z2)⊗2 or |11⟩=(I−Z2)⊗2, meaning the way that the input comes into the system is always limited, i.e., through tensor products of (combinations of) I and Z. Here I is identity operator and Z is Pauli Z. I know this is the conventional way of input-injection in QRC, but considering the fact that the types of input affect the performance, I assume this also have an effect on the precision for probing the property of quantum systems.
It would be nice if the authors clarify this.
・Related to the comment above, I would recommend the authors to examine the effect of hyperparameters such as types of input, evolution time, virtual time, initial state of the ancilla state and so on, because this would be an important point to see if the QRP can robustly produce the property of quantum systems regardless of practitioners' choice of hyperparameters. Then, it would be nice if you summarize this, e.g., in certain section or a paragraph in Discussion and conclusion. If these really affect the performance, I need to rethink the novelty of this work. (I mean, I would like to make sure if the method can perform the task well with only access to practically-reasonable prior knowledge on the system.)
Minor comment
I would recommend to double check typos: I found a typo, e.g., missing punctuation in a sentence “... estimate the information stored in O(τ) Employing...” in line 202, page 7.
Recommendation
Ask for minor revision
Author: Kaito Kobayashi on 2025-02-04 [id 5185]
(in reply to Report 1 on 2025-01-24)Please find the attached response.
Attachment:
reply.pdf