pith. sign in

arxiv: 2605.12713 · v2 · pith:CBBGAAHJnew · submitted 2026-05-12 · 🪐 quant-ph · cs.AI

Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs

Erik L. Connerty , Ethan N. Evans This is my paper

Pith reviewed 2026-05-19 17:19 UTC · model grok-4.3

classification 🪐 quant-ph cs.AI
keywords quantum reservoir computingpartial-SWAPmemory capacityNISQ hardwareamplitude-damping channelrecurrent architecturesquantum reservoir networks
0
0 comments X

The pith

A tunable partial-SWAP mechanism gives direct control over memory dissipation rates in quantum reservoir networks on gate-based hardware.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that recurrent quantum reservoir computing models can be extended with a tunable partial-SWAP operation to adjust how rapidly memory fades from the system. This operation is described as a controlled amplitude-damping channel and is tested through simulation benchmarks and experiments on IBM quantum processors using short-term memory recall tasks and the NARMA-5 dataset. A sympathetic reader would care because earlier recurrent architectures provided fading memory but offered no straightforward hardware knob for setting its duration to suit different computational problems.

Core claim

By inserting a tunable partial-SWAP between memory and readout registers in a multi-register recurrent quantum reservoir network, the rate of memory dissipation becomes directly adjustable via the swap strength parameter, which maps to the damping factor in a controlled amplitude-damping channel. This mechanism is hardware-realizable on gate-based QPUs and enables controllable quantum memory capacity.

What carries the argument

The tunable partial-SWAP, which performs a parameterized exchange of quantum information between registers and thereby induces a controllable amplitude-damping effect that sets the memory fade rate.

If this is right

  • Memory capacity in the reservoir becomes adjustable by changing the single partial-SWAP strength parameter.
  • The same hardware mechanism improves performance on memory-dependent tasks such as NARMA-5 prediction when the dissipation rate is matched to the task.
  • Recurrent architectures gain a missing degree of freedom that was previously fixed by device noise alone.
  • The approach remains compatible with existing gate-based QPU implementations of quantum reservoir networks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same control knob could be used to adapt memory length on the fly as task statistics change during a computation.
  • This tunable dissipation resembles the leaking rate hyperparameter in classical reservoir computing and may transfer similar design practices to the quantum setting.
  • The mechanism might also serve as a basic building block for deliberate noise engineering in other NISQ algorithms that rely on controlled decoherence.

Load-bearing premise

The partial-SWAP acts exactly as a controlled amplitude-damping channel on real NISQ hardware, without extra crosstalk or calibration errors that would block intended control of dissipation.

What would settle it

Running the randomized short-term memory capacity recall benchmark on an IBM QPU while sweeping the partial-SWAP parameter and finding no corresponding change in measured recall performance would falsify the controllability claim.

Figures

Figures reproduced from arXiv: 2605.12713 by Erik L. Connerty, Ethan N. Evans.

Figure 2
Figure 2. Figure 2: The partial-SWAP circuit. A single parameter [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 1
Figure 1. Figure 1: Proposed recurrent partial-SWAP QRN. The circuit is composed of two registers: [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The partial-SWAP circuit. A single parameter [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: STMC Best R2 Comparison for two Configurations of the QRN. Plots depicting the coefficient of determination (R2 ) values for differing nqubits values. Higher is better. A context length of c = 1 is used in this experiment, giving a worst-case demonstration of memory-recall. The best partial-SWAP strength γ was chosen from each nrepeats configuration for the nqubits = 16 circuit in [PITH_FULL_IMAGE:figures… view at source ↗
Figure 4
Figure 4. Figure 4: STMC partial-SWAP Strength Comparison. An ablation study over various parameters of partial-SWAP strength [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: NARMA-5 Comparison for Various Configurations of partial-SWAP QRN. An ablation study over various parameters [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: NARMA-5 Results for both the Aer Simulator and IBM QPU using the partial-SWAP QRN. The NARMA-5 task test [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: partial-SWAP QRN vs Classical ESN on NARMA-5 [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: STMC partial-SWAP Strength Comparison for [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 8
Figure 8. Figure 8: STMC partial-SWAP Strength Comparison for [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: NARMA-5 partial-SWAP Strength Comparison for [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
Figure 9
Figure 9. Figure 9: NARMA-5 partial-SWAP Strength Comparison for [PITH_FULL_IMAGE:figures/full_fig_p014_9.png] view at source ↗
read the original abstract

In the field of quantum reservoir computing (QRC), many different computational models and architectures have been proposed. From these models, we identify feedback-based models -- which use a feedback mechanism to re-embed classical measurements from the QRC -- and recurrent models -- which use a multi-register approach with memory and readout qubits -- as the two major competing architectures that have been discussed and validated on hardware. In this paper, we advance upon the recurrent architectures, which employ a two register approach to endow the QRC with a fading memory. While these approaches have been validated on hardware and have demonstrated great real-world performance on noisy-intermediate-scale-quantum (NISQ) quantum processing units (QPUs), the exact mechanism through which the memory capacity arises is not completely understood or fully controllable. With this, we augment the recurrent approaches and present a hardware-realizable mechanism, which we call a tunable partial-SWAP, that allows for the direct control of the rate of memory dissipation from a QRN implemented on a gate-based QPU. The theory behind this mechanism is discussed in terms of a controlled amplitude-damping channel and validation experiments using a randomized short-term memory capacity (STMC) recall benchmark and the NARMA-5 dataset are conducted using simulation and IBM QPUs, respectively.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript augments recurrent quantum reservoir computing (QRC) architectures by introducing a hardware-realizable tunable partial-SWAP operation in quantum reservoir networks (QRNs). This mechanism is modeled as a controlled amplitude-damping channel that directly controls the rate of memory dissipation; the approach is validated via randomized short-term memory capacity (STMC) recall and NARMA-5 tasks on both simulation and IBM QPUs.

Significance. If the direct-control claim holds, the work supplies a practical, tunable handle on fading memory for recurrent QRC on NISQ hardware, extending prior multi-register designs. The explicit hardware validation on IBM QPUs is a concrete strength that supports reproducibility and applicability.

major comments (2)
  1. Abstract and experimental validation: the reported IBM QPU results for STMC recall and NARMA-5 lack visible error bars, exact circuit depths, gate decompositions of the partial-SWAP, or post-selection details. Because the central claim of direct, predictable memory control rests on these hardware experiments, the absence of these diagnostics leaves open the possibility that observed capacity changes arise from unmodeled crosstalk or calibration drift rather than the intended damping channel.
  2. Modeling section (controlled amplitude-damping channel): the assumption that varying the partial-SWAP tuning parameter produces an isolated, dominant change in memory dissipation rate is load-bearing. Without explicit confirmation that two-qubit error rates and spectator-qubit effects remain constant across the tuning range, or tests isolating the intended channel from other NISQ noise sources, the controllability interpretation is not yet fully secured.
minor comments (2)
  1. Clarify in the theory section how the partial-SWAP is decomposed into native gates and which physical parameter is actually varied for tunability.
  2. Add statistical details (error bars, number of shots, calibration timestamps) to all QPU figures and tables.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which help clarify the presentation of our hardware validation and modeling assumptions. We address each major comment below and indicate the revisions we will make to strengthen the manuscript's support for the controllable memory dissipation claim.

read point-by-point responses

  1. Referee: Abstract and experimental validation: the reported IBM QPU results for STMC recall and NARMA-5 lack visible error bars, exact circuit depths, gate decompositions of the partial-SWAP, or post-selection details. Because the central claim of direct, predictable memory control rests on these hardware experiments, the absence of these diagnostics leaves open the possibility that observed capacity changes arise from unmodeled crosstalk or calibration drift rather than the intended damping channel.

    Authors: We agree that these experimental details are necessary to fully substantiate the hardware results and the interpretation of direct control via the tunable partial-SWAP. In the revised manuscript we will add error bars to all IBM QPU data points for both the randomized STMC recall and NARMA-5 tasks. We will also report the exact circuit depths, provide the explicit gate decomposition of the tunable partial-SWAP (including its implementation as a controlled amplitude-damping channel), and specify any post-selection criteria applied. To address the concern about crosstalk or drift, we will include a new supplementary figure comparing the hardware trends against both ideal and noisy simulations that incorporate only the modeled damping channel; the close agreement supports that the observed capacity variation is dominated by the intended mechanism rather than unmodeled hardware effects. revision: yes

  2. Referee: Modeling section (controlled amplitude-damping channel): the assumption that varying the partial-SWAP tuning parameter produces an isolated, dominant change in memory dissipation rate is load-bearing. Without explicit confirmation that two-qubit error rates and spectator-qubit effects remain constant across the tuning range, or tests isolating the intended channel from other NISQ noise sources, the controllability interpretation is not yet fully secured.

    Authors: We acknowledge the importance of securing this modeling assumption. The partial-SWAP is constructed so that its tuning parameter directly modulates the damping strength on the memory register while leaving the computational register largely unaffected. In the revised manuscript we will augment the modeling section with calibration data from the IBM QPUs showing that the relevant two-qubit error rates remain approximately constant over the explored tuning range. We will also add simulation results that isolate the partial-SWAP effect by comparing reservoir dynamics with the tunable operation turned on versus off under identical noise models; these tests demonstrate that the change in memory dissipation rate tracks the intended controlled amplitude-damping channel. While complete isolation from all spectator and crosstalk effects is challenging on current NISQ devices, the combination of theory, simulation, and hardware consistency provides a solid basis for the controllability claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity; tunable partial-SWAP control derived independently from channel model and hardware validation

full rationale

The paper augments prior recurrent QRN architectures by introducing a hardware-realizable tunable partial-SWAP, modeled explicitly as a controlled amplitude-damping channel whose damping parameter directly sets the memory dissipation rate. This modeling step precedes and is independent of the subsequent STMC recall and NARMA-5 validation experiments performed in simulation and on IBM QPUs. No equation or claim reduces the reported memory-capacity control to a parameter fitted from the same benchmark data, nor does any load-bearing step rely on a self-citation whose content is itself defined by the present result. The derivation chain therefore remains self-contained against external benchmarks and does not exhibit self-definitional, fitted-input, or ansatz-smuggling circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The work rests on standard quantum-circuit assumptions plus the modeling choice that partial-SWAP equals controlled amplitude damping; a tunable strength parameter is introduced to set the dissipation rate.

free parameters (1)
  • partial-SWAP tuning parameter
    Adjustable strength of the partial swap that sets the memory dissipation rate; chosen or calibrated to achieve desired memory lifetime.
axioms (1)
  • domain assumption The tunable partial-SWAP operation can be faithfully modeled as a controlled amplitude-damping channel on NISQ hardware.
    This modeling choice underpins the theoretical controllability claim in the abstract.
invented entities (1)
  • tunable partial-SWAP no independent evidence
    purpose: Provide direct, hardware-realizable control over memory dissipation rate in recurrent quantum reservoir networks.
    New operation introduced by the authors; independent evidence would require external verification of the damping model on other hardware.

pith-pipeline@v0.9.0 · 5760 in / 1352 out tokens · 66416 ms · 2026-05-19T17:19:20.435333+00:00 · methodology

Review history (2 revisions) →

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

36 extracted references · 36 canonical work pages

  1. [1]

    Sori- ano, and Roberta Zambrini

    Pere Mujal, Rodrigo Martínez-Peña, Johannes Nokkala, Jorge García-Beni, Gian Luca Giorgi, Miguel C. Sori- ano, and Roberta Zambrini. Opportunities in Quantum Reservoir Computing and Extreme Learning Machines. Advanced Quantum Technologies, 4(8):2100027, August

    work page

  2. [2]

    Quantum Circuit Design using a Progres- sive Widening Enhanced Monte Carlo Tree Search,

    ISSN 2511-9044, 2511-9044. doi: 10.1002/qute. 7 Name Parameter Value Total # of Qubits nqubits {2,4,6,8,10,12,14,16} partial-SW AP Strength γ{.05, .1, .15, . . . ,1.0} # of Shots nshots 30,000 # of Re-uploading Blocks nrepeats {1,3} # of Total Data Points ntotal 1000 # of Training Data Points ntrain 700 # of Test Data Points ntrain 275 # of Washout Points...

    work page doi:10.1002/qute

  3. [3]

    Experimental demonstra- tion of enhanced quantum tomography via quantum reser- voir processing.Quantum Science and Technology, 10 (3):035041, June 2025

    Tanjung Krisnanda, Pengtao Song, Adrian Copetudo, Clara Yun Fontaine, Tomasz Paterek, Timothy C H Liew, and Yvonne Y Gao. Experimental demonstra- tion of enhanced quantum tomography via quantum reser- voir processing.Quantum Science and Technology, 10 (3):035041, June 2025. ISSN 2058-9565. doi: 10. 1088/2058-9565/addffe. URL https://doi.org/10. 1088/2058-...

    work page 2025

  4. [4]

    arXiv preprint arXiv:2407.02553 , year=

    Milan Kornjaˇca, Hong-Ye Hu, Chen Zhao, Jonathan Wurtz, Phillip Weinberg, Majd Hamdan, Andrii Zhdanov, Ser- gio H Cantu, Hengyun Zhou, Rodrigo Araiza Bravo, et al. Large-scale quantum reservoir learning with an analog quantum computer.arXiv:2407.02553, 2024

    work page arXiv 2024

  5. [5]

    Khan, Nicholas T

    Fangjun Hu, Saeed A. Khan, Nicholas T. Bronn, Gerasimos Angelatos, Graham E. Rowlands, Guilhem J. Ribeill, and Hakan E. Türeci. Overcoming the coherence time barrier in quantum machine learning on temporal data.Nature Communications, 15(1):7491, Aug 2024. ISSN 2041-1723. doi: 10.1038/s41467-024-51162-7. URL https://doi. org/10.1038/s41467-024-51162-7

    work page doi:10.1038/s41467-024-51162-7 2024

  6. [6]

    Osama Ahmed, Felix Tennie, and Luca Magri. Robust quantum reservoir computers for forecasting chaotic dy- namics: generalized synchronization and stability.Pro- ceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 481(2324):20250550, 10 2025. ISSN 1364-5021. doi: 10.1098/rspa.2025.0550. URL https://doi.org/10.1098/rspa.2025.0550

    work page doi:10.1098/rspa.2025.0550 2025

  7. [7]

    and Yamashiro, Y

    Jakob Murauer, Rajiv Krishnakumar, Sabine Tornow, and Michaela Geierhos. Feedback connections in quan- tum reservoir computing with mid-circuit measurements. In2025 IEEE International Conference on Quantum Computing and Engineering (QCE), page 1646–1652. IEEE, August 2025. doi: 10.1109/qce65121.2025.00182. URL http://dx.doi.org/10.1109/QCE65121. 2025.00182

    work page doi:10.1109/qce65121.2025.00182 2025

  8. [8]

    Feedback-driven quantum reservoir com- puting for time-series analysis.PRX Quantum, 5: 040325, Nov 2024

    Kaito Kobayashi, Keisuke Fujii, and Naoki Ya- mamoto. Feedback-driven quantum reservoir com- puting for time-series analysis.PRX Quantum, 5: 040325, Nov 2024. doi: 10.1103/PRXQuantum.5. 040325. URL https://link.aps.org/doi/10. 1103/PRXQuantum.5.040325

    work page doi:10.1103/prxquantum.5 2024

  9. [9]

    Enhancing the performance of quantum reser- voir computing and solving the time-complexity prob- lem by artificial memory restriction.Phys

    Saud ˇCindrak, Brecht Donvil, Kathy Lüdge, and Lina Jaurigue. Enhancing the performance of quantum reser- voir computing and solving the time-complexity prob- lem by artificial memory restriction.Phys. Rev. Res., 6:013051, Jan 2024. doi: 10.1103/PhysRevResearch.6. 013051. URL https://link.aps.org/doi/10. 1103/PhysRevResearch.6.013051

    work page doi:10.1103/physrevresearch.6 2024

  10. [10]

    Hybrid quantum-classical reservoir comput- ing of thermal convection flow.Phys

    Philipp Pfeffer, Florian Heyder, and Jörg Schu- macher. Hybrid quantum-classical reservoir comput- ing of thermal convection flow.Phys. Rev. Res., 4: 033176, Sep 2022. doi: 10.1103/PhysRevResearch.4. 033176. URL https://link.aps.org/doi/10. 1103/PhysRevResearch.4.033176

    work page doi:10.1103/physrevresearch.4 2022

  11. [11]

    Connerty, Ethan N

    Erik L. Connerty, Ethan N. Evans, Gerasimos Ange- latos, and Vignesh Narayanan. Predicting chaotic dy- namics on nisq hardware with quantum reservoir net- works.Communications Physics, May 2026. ISSN 2399-

    work page 2026

  12. [12]

    URL https: //doi.org/10.1038/s42005-026-02652-1

    doi: 10.1038/s42005-026-02652-1. URL https: //doi.org/10.1038/s42005-026-02652-1

    work page doi:10.1038/s42005-026-02652-1

  13. [13]

    Soriano, and Roberta Zambrini

    Pere Mujal, Rodrigo Martínez-Peña, Gian Luca Giorgi, Miguel C. Soriano, and Roberta Zambrini. Time- series quantum reservoir computing with weak and pro- jective measurements.npj Quantum Information, 9 (1):16, Feb 2023. ISSN 2056-6387. doi: 10.1038/ s41534-023-00682-z. URL https://doi.org/10. 1038/s41534-023-00682-z. 8

    work page 2023

  14. [14]

    Nurdin, and Naoki Yamamoto

    Toshiki Yasuda, Yudai Suzuki, Tomoyuki Kubota, Kohei Nakajima, Qi Gao, Wenlong Zhang, Satoshi Shimono, Hen- dra I. Nurdin, and Naoki Yamamoto. Quantum reservoir computing with repeated measurements on superconduct- ing devices, 2023. URL https://arxiv.org/abs/ 2310.06706

    work page arXiv 2023

  15. [15]

    Feedback-enhanced quantum reservoir com- puting with weak measurements, 2025

    Tomoya Monomi, Wataru Setoyama, and Yoshihiko Hasegawa. Feedback-enhanced quantum reservoir com- puting with weak measurements, 2025. URL https: //arxiv.org/abs/2503.17939

    work page arXiv 2025

  16. [16]

    Ehlers, Hendra I

    Chuanzhou Zhu, Peter J. Ehlers, Hendra I. Nurdin, and Daniel Soh. Minimalistic and scalable quantum reser- voir computing enhanced with feedback.npj Quantum Information, 11(1):195, Nov 2025. ISSN 2056-6387. doi: 10.1038/s41534-025-01144-4. URL https://doi. org/10.1038/s41534-025-01144-4

    work page doi:10.1038/s41534-025-01144-4 2025

  17. [17]

    Experimen- tal memory control in continuous-variable optical quan- tum reservoir computing.Nat

    Iris Paparelle, Johan Henaff, Jorge García-Beni, Émilie Gillet, Daniel Montesinos, Gian Luca Giorgi, Miguel C So- riano, Roberta Zambrini, and Valentina Parigi. Experimen- tal memory control in continuous-variable optical quan- tum reservoir computing.Nat. Photonics, 20(4):413–420, March 2026

    work page 2026

  18. [18]

    Feedback-driven recurrent quantum neural network universality, 2026

    Lukas Gonon, Rodrigo Martínez-Peña, and Juan-Pablo Ortega. Feedback-driven recurrent quantum neural network universality, 2026. URL https://arxiv.org/abs/ 2506.16332

    work page arXiv 2026

  19. [19]

    W. D. Kalfus, G. J. Ribeill, G. E. Rowlands, H. K. Krovi, T. A. Ohki, and L. C. G. Govia. Hilbert space as a com- putational resource in reservoir computing.Phys. Rev. Res., 4:033007, Jul 2022. doi: 10.1103/PhysRevResearch. 4.033007. URL https://link.aps.org/doi/10. 1103/PhysRevResearch.4.033007

    work page doi:10.1103/physrevresearch 2022

  20. [20]

    Schuld (2021), arXiv:2101.11020 [quant-ph]

    Maria Schuld. Supervised quantum machine learning models are kernel methods, January 2021. URL https: //arxiv.org/abs/2101.11020v2

    work page arXiv 2021

  21. [21]

    Sanjib Ghosh, Andrzej Opala, Michal Matuszewski, Tomasz Paterek, and Timothy C. H. Liew. Reconstructing quantum states with quantum reservoir networks.IEEE Transactions on Neural Networks and Learning Systems, 32(7):3148–3155, July 2021. ISSN 2162-2388. doi: 10.1109/tnnls.2020.3009716. URL http://dx.doi. org/10.1109/TNNLS.2020.3009716

    work page doi:10.1109/tnnls.2020.3009716 2021

  22. [22]

    Khan, and Hakan E

    Gerasimos Angelatos, Saeed A. Khan, and Hakan E. Türeci. Reservoir computing approach to quantum state measurement.Phys. Rev. X, 11:041062, Dec 2021. doi: 10.1103/PhysRevX.11.041062. URL https://link. aps.org/doi/10.1103/PhysRevX.11.041062

    work page doi:10.1103/physrevx.11.041062 2021

  23. [23]

    Khan, Marti Vives, Esin Türeci, Leon Bello, Graham E

    Fangjun Hu, Gerasimos Angelatos, Saeed A. Khan, Marti Vives, Esin Türeci, Leon Bello, Graham E. Rowlands, Guilhem J. Ribeill, and Hakan E. Türeci. Tackling sam- pling noise in physical systems for machine learning appli- cations: Fundamental limits and eigentasks.Phys. Rev. X, 13:041020, Oct 2023. doi: 10.1103/PhysRevX.13. 041020. URL https://link.aps.org...

    work page doi:10.1103/physrevx.13 2023

  24. [24]

    Nielsen and Isaac L

    Michael A. Nielsen and Isaac L. Chuang.Quantum Computation and Quantum Information: 10th Anniver- sary Edition. Cambridge University Press, 2011. ISBN 9781107002173

    work page 2011

  25. [25]

    Decoherence free subspaces for quantum communication in amplitude damping channels, 04 2020

    Nimish Mishra, Bikash Behera, and Prasanta Panigrahi. Decoherence free subspaces for quantum communication in amplitude damping channels, 04 2020

    work page 2020

  26. [26]

    So- riano, Gian Luca Giorgi, and Roberta Zambrini

    Antonio Sannia, Rodrigo Martínez-Peña, Miguel C. So- riano, Gian Luca Giorgi, and Roberta Zambrini. Dis- sipation as a resource for Quantum Reservoir Comput- ing.Quantum, 8:1291, March 2024. ISSN 2521-327X. doi: 10.22331/q-2024-03-20-1291. URL https://doi. org/10.22331/q-2024-03-20-1291

    work page doi:10.22331/q-2024-03-20-1291 2024

  27. [27]

    Hamiltonian-driven architectures for non-markovian quantum reservoir comput- ing, 2025

    Daiki Sasaki, Ryosuke Koga, Taihei Kuroiwa, Yuya Ito, Chih-Chieh Chen, and Tomah Sogabe. Hamiltonian-driven architectures for non-markovian quantum reservoir comput- ing, 2025. URL https://arxiv.org/abs/2505. 14450

    work page 2025

  28. [28]

    Non-markovianity and memory enhancement in quantum reservoir computing

    Antonio Sannia, Ricard Ravell Rodríguez, Gian Luca Giorgi, and Roberta Zambrini. Non-markovianity and memory enhancement in quantum reservoir computing. npj Quantum Information, May 2026. ISSN 2056-6387. doi: 10.1038/s41534-026-01257-4. URL https://doi. org/10.1038/s41534-026-01257-4

    work page doi:10.1038/s41534-026-01257-4 2026

  29. [29]

    ‘the ‘echo state’ approach to analysing and train- ing recurrent neural networks,”german nat.Res

    H Jaeger. ‘the ‘echo state’ approach to analysing and train- ing recurrent neural networks,”german nat.Res. Center Inf. Technol., GMD Rep, 148:43, 2001

    work page 2001

  30. [30]

    Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil- Fuster, and José I. Latorre. Data re-uploading for a universal quantum classifier.Quantum, 4:226, February 2020. ISSN 2521-327X. doi: 10.22331/ q-2020-02-06-226. URL http://dx.doi.org/10. 22331/q-2020-02-06-226

    work page 2020

  31. [31]

    Qmlp: An error-tolerant nonlinear quantum mlp architecture using parameterized two-qubit gates, 2022

    Cheng Chu, Nai-Hui Chia, Lei Jiang, and Fan Chen. Qmlp: An error-tolerant nonlinear quantum mlp architecture using parameterized two-qubit gates, 2022. URL https:// arxiv.org/abs/2206.01345

    work page arXiv 2022

  32. [32]

    Nonlinear input transformations are ubiquitous in quantum reservoir computing.Neuromorphic Com- puting and Engineering, 2(1):014008, feb 2022

    L C G Govia, G J Ribeill, G E Rowlands, and T A Ohki. Nonlinear input transformations are ubiquitous in quantum reservoir computing.Neuromorphic Com- puting and Engineering, 2(1):014008, feb 2022. doi: 10.1088/2634-4386/ac4fcd. URL https://dx.doi. org/10.1088/2634-4386/ac4fcd

    work page doi:10.1088/2634-4386/ac4fcd 2022

  33. [33]

    On fundamental aspects of quantum extreme learning machines.Quantum Machine Intelligence, 7 (1):20, Feb 2025

    Weijie Xiong, Giorgio Facelli, Mehrad Sahebi, Owen Agnel, Thiparat Chotibut, Supanut Thanasilp, and Zoë Holmes. On fundamental aspects of quantum extreme learning machines.Quantum Machine Intelligence, 7 (1):20, Feb 2025. ISSN 2524-4914. doi: 10.1007/ s42484-025-00239-7. URL https://doi.org/10. 1007/s42484-025-00239-7. 9

    work page 2025

  34. [34]

    Expressiv- ity limits of quantum reservoir computing, 2025

    Nils-Erik Schütte, Niclas Götting, Hauke Müntinga, Meike List, Daniel Brunner, and Christopher Gies. Expressiv- ity limits of quantum reservoir computing, 2025. URL https://arxiv.org/abs/2501.15528

    work page arXiv 2025

  35. [35]

    Wood, Jake Lishman, Julien Gacon, Simon Martiel, Paul D

    Ali Javadi-Abhari, Matthew Treinish, Kevin Krsulich, Christopher J. Wood, Jake Lishman, Julien Gacon, Simon Martiel, Paul D. Nation, Lev S. Bishop, Andrew W. Cross, Blake R. Johnson, and Jay M. Gambetta. Quantum com- puting with Qiskit, 2024

    work page 2024

  36. [36]

    Ridge regression: Biased estimation for nonorthogonal problems.Technomet- rics, 12(1):55–67, 1970

    Arthur E Hoerl and Robert W Kennard. Ridge regression: Biased estimation for nonorthogonal problems.Technomet- rics, 12(1):55–67, 1970. 10 A Supplementary Note 1: Amplitude Damping Channel with partial-SW AP Here we show that the partial-SWAP with measure-and-reset creates an amplitude damping channel. This important property is what allows the partial-SW...

    work page 1970