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arxiv: 2604.25663 · v1 · submitted 2026-04-28 · 🪐 quant-ph

Recognition: unknown

Quantum memory and scrambling from the perspective of a classical neural network

Dimitrios Maroulakos , Andrzej Wal , Marcin Kowalik , Czes{\l}aw Jasiukiewicz , Rohit Kumar Shukla , Sunil K. Mishra , Levan Chotorlishvili
Authors on Pith no claims yet

Pith reviewed 2026-05-07 16:30 UTC · model grok-4.3

classification 🪐 quant-ph
keywords quantum memoryOTOCneural networkspin chainquantum correlationssymmetry breakingnonreciprocal effectstime-dependent
0
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The pith

Time-dependent quantum memory oscillates faster than the out-of-time-ordered correlator and does not equilibrate.

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

The authors extend the concept of quantum memory to time-dependent scenarios to examine the evolution of quantum correlations in physical systems such as atomic helical spin chains. By contrasting this with the out-of-time-ordered correlator, they establish that quantum memory displays quicker oscillations over time and does not settle to an equilibrium value. Training a classical neural network to forecast these quantities demonstrates that quantum memory detects changes in inversion symmetry and nonreciprocal interactions more readily than the correlator measure does. This method makes it feasible to analyze quantum memory in systems that would otherwise demand prohibitive computational resources.

Core claim

In this work the authors propose a time-dependent version of quantum memory and apply it to the atomic helical spin chain to track the emergence and propagation of quantum correlations. The results indicate that quantum memory exhibits faster oscillations in time than the out-of-time-ordered correlator and does not equilibrate. Furthermore, predictions from a trained artificial neural network show quantum memory to be more sensitive than the out-of-time-ordered correlator to broken inversion symmetry and the nonreciprocal effect.

What carries the argument

The time-dependent formulation of quantum memory, which quantifies how prior quantum correlations reduce measurement uncertainties in evolving systems, combined with neural-network approximation, forms the central machinery for comparing it to scrambling indicators like the OTOC.

Load-bearing premise

The proposal assumes that the modified time-dependent quantum memory remains a rigorously defined mathematical quantity that accurately captures the intended correlations without introducing inconsistencies.

What would settle it

Computing the exact time-dependent quantum memory for a small helical spin chain and checking whether the neural network predictions match within acceptable error bounds would confirm or refute the method's accuracy.

Figures

Figures reproduced from arXiv: 2604.25663 by Andrzej Wal, Czes{\l}aw Jasiukiewicz, Dimitrios Maroulakos, Levan Chotorlishvili, Marcin Kowalik, Rohit Kumar Shukla, Sunil K. Mishra.

Figure 1
Figure 1. Figure 1: The proposed model mimics the experimental setup of the work [17]. The atomic chain with a length of up to view at source ↗
Figure 2
Figure 2. Figure 2: The pictorial plot of the artificial neurons which are used to predict the time-dependence of OTOC and quantum view at source ↗
Figure 3
Figure 3. Figure 3: Schematic of an ANN used to predict OTOC view at source ↗
Figure 4
Figure 4. Figure 4: Plot for the left hand side (amber) and the right hand side (blue), as functions of time for the inequality view at source ↗
Figure 5
Figure 5. Figure 5: Plot for C +(t), in (12) and the first excited state n1 = 1, where we take |φni ⟩ ≡ ni . The spin chain has length, L = 10 and perform summation over all states n2, n3, n4. For the coefficient An3n4 n1n2 , use expressions in (46)-(49), taking into account (4).For phase we use (50). The time unit is set by |J1|/ℏ. ⟨n4| σˆ z 1 |n1⟩ = 1 L X L j=1 e i2π(n4−n1)j/L (1 − 2δ1,j ), (49) and E ± n1 − E ± n2 + E ± n3… view at source ↗
Figure 6
Figure 6. Figure 6: OTOC as a function of time for two excitation case. The number of the spins view at source ↗
Figure 7
Figure 7. Figure 7: OTOC as a function of time for two excitation case and nearest neighbor coupling. The number of the spins view at source ↗
Figure 8
Figure 8. Figure 8: The exact (ground truth) and the predicted via neural network values of OTOC as a function of time. For view at source ↗
Figure 9
Figure 9. Figure 9: The time-dependent quantum memory for anticlockwise (LHS) propagating spin excitations. The values of the view at source ↗
Figure 10
Figure 10. Figure 10: The time-dependent quantum memory for anticlockwise (LHS) propagating spin excitations. The values of view at source ↗
read the original abstract

Entropic uncertainty relations are universal quantifiers of fundamental uncertainties of quantum measurements and are widely discussed in the quantum metrology literature. Quantum memory is a phenomenon related to the specific type of quantum correlations that allows for reducing fundamental uncertainties of quantum measurements. In the present work, the modified concept of quantum memory for time-dependent problems is proposed. We compare the time-dependent formulation of quantum memory with the out-of-time-ordered correlator (OTOC). Quantum memory is a rigorous mathematical concept that requires demanding calculations. Thus, until now, quantum memory has been discussed mainly for simple model systems and stationary problems. In the present work, we demonstrate that quantum memory can also be studied for realistic and physically relevant systems, e.g., the atomic helical spin chain, as well as the emergence and propagation of quantum correlations in time. We found that quantum memory manifests faster oscillations in time than OTOC and does not equilibrate. Furthermore, an artificial neural network is trained and asked to predict results for OTOC and quantum memory. These results show that quantum memory is more sensitive than OTOC in terms of broken inversion symmetry and the nonreciprocal effect.

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

3 major / 1 minor

Summary. The paper proposes a modified time-dependent extension of quantum memory based on entropic uncertainty relations, compares its dynamics to the out-of-time-ordered correlator (OTOC) in an atomic helical spin chain, and reports that quantum memory exhibits faster oscillations in time, fails to equilibrate, and is more sensitive to broken inversion symmetry and nonreciprocal effects. A classical neural network is trained to predict both quantities for these systems.

Significance. If the time-dependent quantum memory is placed on a rigorous footing and the neural-network results are shown to be reproducible and unbiased, the work would usefully extend quantum-memory concepts beyond stationary cases to realistic time-dependent models and illustrate the utility of machine learning for computationally intensive quantum-information observables.

major comments (3)
  1. [Abstract] Abstract: the central claim that 'quantum memory manifests faster oscillations in time than OTOC and does not equilibrate' rests on the proposed time-dependent modification, yet the manuscript supplies no explicit formula, derivation from conditional von Neumann entropy, or adjustment for time evolution (e.g., time-ordered exponentials or instantaneous versus integrated entropies).
  2. [Neural network results] Neural-network results paragraph: no training protocol, data source, validation metrics, baseline comparisons, or error analysis are described, so it is impossible to determine whether the reported greater sensitivity of quantum memory to inversion-symmetry breaking and nonreciprocity constitutes an independent test or a re-expression of fitted parameters.
  3. [Results and discussion] Comparison claims: without a demonstrated link between the time-dependent quantum memory and the underlying entropic uncertainty relation for evolving states, the asserted differences from OTOC cannot be interpreted as physical rather than definitional artifacts.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'emergence and propagation of quantum correlations in time' is used without indicating the concrete observable employed beyond quantum memory and OTOC.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thorough review and valuable suggestions. We address each of the major comments below and outline the revisions we plan to implement.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'quantum memory manifests faster oscillations in time than OTOC and does not equilibrate' rests on the proposed time-dependent modification, yet the manuscript supplies no explicit formula, derivation from conditional von Neumann entropy, or adjustment for time evolution (e.g., time-ordered exponentials or instantaneous versus integrated entropies).

    Authors: We agree that the abstract lacks the explicit formula for the time-dependent quantum memory. The main text proposes the modification based on the conditional von Neumann entropy for time-evolved states. In the revised manuscript, we will include the explicit expression for the time-dependent quantum memory, along with a derivation sketch from the entropic uncertainty relation, clarifying the use of instantaneous entropies. This will make the central claim more transparent. revision: yes

  2. Referee: [Neural network results] Neural-network results paragraph: no training protocol, data source, validation metrics, baseline comparisons, or error analysis are described, so it is impossible to determine whether the reported greater sensitivity of quantum memory to inversion-symmetry breaking and nonreciprocity constitutes an independent test or a re-expression of fitted parameters.

    Authors: The referee is correct that the neural network details were omitted. We will add a new subsection detailing the training protocol, including the data generation from exact diagonalization of the helical spin chain, the neural network architecture (e.g., feedforward layers), training/validation split, loss function, and performance metrics such as mean squared error. We will also include baseline comparisons with simpler regression models and error analysis to demonstrate that the sensitivity findings are robust and not artifacts of the fitting. revision: yes

  3. Referee: [Results and discussion] Comparison claims: without a demonstrated link between the time-dependent quantum memory and the underlying entropic uncertainty relation for evolving states, the asserted differences from OTOC cannot be interpreted as physical rather than definitional artifacts.

    Authors: We maintain that the time-dependent quantum memory is directly linked to the entropic uncertainty relation via the conditional entropy of time-evolved states, providing a physical basis for the observed differences. The faster oscillations and lack of equilibration reflect the memory-assisted uncertainty reduction in dynamical settings, which differs from the operator spreading captured by OTOC. To strengthen this, we will expand the discussion section with an explicit derivation step showing the connection to the uncertainty relation for evolving states and additional numerical evidence supporting the physical interpretation. revision: partial

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper proposes a time-dependent extension of quantum memory as a modified concept, performs direct calculations on the helical spin chain to compare its time evolution against OTOC, and employs a neural network as a computational tool to explore predictions across parameter regimes (including symmetry breaking). No equations or sections reduce a claimed prediction or result to an input by construction, no self-citation chain bears the central claim, and the neural-network step is presented as an auxiliary predictor rather than a fitted re-expression of the same quantities used to define quantum memory. The absence of an explicit derivation for the time-dependent modification is a potential definitional gap but does not create circularity under the specified criteria; the reported differences in oscillation speed and sensitivity remain independent outputs of the model.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claims rest on the validity of extending entropic uncertainty relations to explicitly time-dependent operators and on the assumption that a classical neural network can serve as a faithful surrogate for the quantum calculations; both are domain assumptions whose justification is not supplied in the abstract.

free parameters (1)
  • neural-network architecture and training hyperparameters
    Chosen or fitted to reproduce the quantum-memory and OTOC time series; no values or selection procedure given.
axioms (1)
  • domain assumption Entropic uncertainty relations remain valid when operators are made explicitly time-dependent
    Invoked to define the time-dependent quantum memory but not re-derived in the abstract.

pith-pipeline@v0.9.0 · 5527 in / 1415 out tokens · 63148 ms · 2026-05-07T16:30:09.700011+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

104 extracted references · 3 canonical work pages · 1 internal anchor

  1. [1]

    Bersin, M

    E. Bersin, M. Sutula, Y. Q. Huan, A. Suleymanzade, D. R. Assumpcao, Y.-C. Wei, P.-J. Stas, C. M. Knaut, E. N. Knall, C. Langrock, N. Sinclair, R. Murphy, R. Riedinger, M. Yeh, C. Xin, S. Bandyopadhyay, D. D. Sukachev, B. Machielse, D. S. Levonian, M. K. Bhaskar, S. Hamilton, H. Park, M. Lončar, M. M. Fejer, P. B. Dixon, D. R. Englund, M. D. Lukin,PRX Quan...

  2. [2]

    R. Fan, Y. Bao, E. Altman, A. Vishwanath,PRX Quantum2024,5020343

  3. [3]

    Sekatski, J.-D

    P. Sekatski, J.-D. Bancal, M. Ioannou, M. Afzelius, N. Brunner,Phys. Rev. Lett.2023,131 170802

    2023

  4. [4]

    Davidson, O

    O. Davidson, O. Yogev, E. Poem, O. Firstenberg,Phys. Rev. Lett.2023,131033601

    2023

  5. [5]

    Drmota, D

    P. Drmota, D. Main, D. P. Nadlinger, B. C. Nichol, M. A. Weber, E. M. Ainley, A. Agrawal, R. Srinivas, G. Araneda, C. J. Ballance, D. M. Lucas,Phys. Rev. Lett.2023,130090803

    2023

  6. [6]

    D. Wang, F. Ming, M.-L. Hu, L. Ye,Annalen der Physik2019,531, 10 1900124. 22 REFERENCES REFERENCES

  7. [7]

    F. Ming, D. Wang, X.-G. Fan, W.-N. Shi, L. Ye, J.-L. Chen,Physical Review A2020,102, 1 012206

  8. [8]

    Dolatkhah, S

    H. Dolatkhah, S. Haseli, S. Salimi, A. Khorashad,Physical Review A2020,102, 5 052227

  9. [9]

    Bergh, M

    B. Bergh, M. Gärttner,Physical Review A2021,103, 5 052412

  10. [10]

    B.-F. Xie, F. Ming, D. Wang, L. Ye, J.-L. Chen,Phys. Rev. A2021,104062204

  11. [11]

    Chotorlishvili, A

    L. Chotorlishvili, A. Gudyma, J. Wätzel, A. Ernst, J. Berakdar,Physical Review B2019,100, 17 174413

  12. [12]

    Song, L.-J

    M.-L. Song, L.-J. Li, X.-K. Song, L. Ye, D. Wang,Physical Review E2022,106, 5 054107

  13. [13]

    Zhu,npj Quantum Information2021,7, 1 47

    H. Zhu,npj Quantum Information2021,7, 1 47

  14. [14]

    Kurashvili, L

    P. Kurashvili, L. Chotorlishvili, K. Kouzakov, A. Studenikin,The European Physical Journal Plus 2022,137, 2 234

    2022

  15. [15]

    Kurashvili, L

    P. Kurashvili, L. Chotorlishvili, K. A. Kouzakov, A. G. Tevzadze, A. I. Studenikin,Phys. Rev. D 2021,103036011

    2021

  16. [16]

    Berta, M

    M. Berta, M. Christandl, R. Colbeck, J. M. Renes, R. Renner,Nature Physics2010,6, 9 659

  17. [17]

    Menzel, Y

    M. Menzel, Y. Mokrousov, R. Wieser, J. E. Bickel, E. Vedmedenko, S. Blügel, S. Heinze, K. von Bergmann, A. Kubetzka, R. Wiesendanger,Phys. Rev. Lett.2012,108197204

    2012

  18. [18]

    Hammer, W

    L. Hammer, W. Meier, A. Schmidt, K. Heinz,Phys. Rev. B2003,67125422

  19. [19]

    Heyl,Reports on Progress in Physics2018,81, 5 054001

    M. Heyl,Reports on Progress in Physics2018,81, 5 054001

  20. [20]

    M. Heyl, F. Pollmann, B. Dóra,Phys. Rev. Lett.2018,121016801

    2018

  21. [21]

    M. Heyl, A. Polkovnikov, S. Kehrein,Phys. Rev. Lett.2013,110135704

    2013

  22. [22]

    R. Vosk, E. Altman,Phys. Rev. Lett.2014,112217204

    2014

  23. [23]

    Eisert, M

    J. Eisert, M. Friesdorf, C. Gogolin,Nature Physics2015,11, 2 124–130

  24. [24]

    Ponte, Z

    P. Ponte, Z. Papić, F. Huveneers, D. A. Abanin,Phys. Rev. Lett.2015,114140401

    2015

  25. [25]

    Azimi, L

    M. Azimi, L. Chotorlishvili, S. K. Mishra, S. Greschner, T. Vekua, J. Berakdar,Phys. Rev. B 2014,89024424

    2014

  26. [26]

    Azimi, M

    M. Azimi, M. Sekania, S. K. Mishra, L. Chotorlishvili, Z. Toklikishvili, J. Berakdar,Phys. Rev. B2016,94064423

  27. [27]

    E. H. Lieb, D. W. Robinson,Communications in Mathematical Physics1972,28, 3 251–257

  28. [28]

    A. I. Larkin, Y. N. Ovchinnikov,Journal of Experimental and Theoretical Physics1969,28, 6 1200

  29. [29]

    Maldacena, S

    J. Maldacena, S. H. Shenker, D. Stanford,Journal of High Energy Physics2016,2016, 8 106

    2016

  30. [30]

    D. A. Roberts, D. Stanford, L. Susskind,Journal of High Energy Physics2015,2015, 3 51

    2015

  31. [31]

    Iyoda, T

    E. Iyoda, T. Sagawa,Phys. Rev. A2018,97042330

  32. [32]

    Chapman, A

    A. Chapman, A. Miyake,Phys. Rev. A2018,98012309

  33. [33]

    Swingle, D

    B. Swingle, D. Chowdhury,Phys. Rev. B2017,95060201

  34. [34]

    M. J. Klug, M. S. Scheurer, J. Schmalian,Phys. Rev. B2018,98045102. 23 REFERENCES REFERENCES

  35. [35]

    del Campo, J

    A. del Campo, J. Molina-Vilaplana, J. Sonner,Phys. Rev. D2017,95126008

  36. [36]

    Campisi, J

    M. Campisi, J. Goold,Phys. Rev. E2017,95062127

  37. [37]

    Grozdanov, K

    S. Grozdanov, K. Schalm, V. Scopelliti,Phys. Rev. Lett.2018,120231601

    2018

  38. [38]

    A. A. Patel, D. Chowdhury, S. Sachdev, B. Swingle,Phys. Rev. X2017,7031047

  39. [39]

    Khemani, A

    V. Khemani, A. Vishwanath, D. A. Huse,Phys. Rev. X2018,8031057

  40. [40]

    R. K. Shukla, A. Lakshminarayan, S. K. Mishra,Phys. Rev. B2022,105224307

  41. [41]

    R. K. Shukla, S. K. Mishra,Phys. Rev. A2022,106022403

  42. [42]

    Rakovszky, F

    T. Rakovszky, F. Pollmann, C. W. von Keyserlingk,Phys. Rev. X2018,8031058

  43. [43]

    S. V. Syzranov, A. V. Gorshkov, V. Galitski,Phys. Rev. B2018,97161114

  44. [44]

    Hosur, X.-L

    P. Hosur, X.-L. Qi, D. A. Roberts, B. Yoshida,Journal of High Energy Physics2016,2016, 2 4

    2016

  45. [45]

    Yunger Halpern,Phys

    N. Yunger Halpern,Phys. Rev. A2017,95012120

  46. [46]

    D. A. Roberts, B. Yoshida,Journal of High Energy Physics2017,2017, 4

    2017

  47. [47]

    Lo Monaco, L

    G. Lo Monaco, L. Innocenti, D. Cilluffo, D. A. Chisholm, S. Lorenzo, G. M. Palma,Quantum Science and Technology2023,8, 3 035006

  48. [48]

    Lo Monaco, L

    G. Lo Monaco, L. Innocenti, D. Cilluffo, D. A. Chisholm, S. Lorenzo, G. Massimo Palma,Quantum Science and Technology2024,10, 1 015055

  49. [49]

    Touil, S

    A. Touil, S. Deffner,Quantum Science and Technology2020,5, 3 035005

  50. [50]

    Zhuang, Y.-K

    J.-Z. Zhuang, Y.-K. Wu, L.-M. Duan,Physical Review B2022,106, 14 144308

  51. [51]

    M. A. Nielsen,Neural networks and deep learning, volume 25, Determination press San Francisco, CA, USA,2015

    2015

  52. [52]

    K. Beer, D. Bondarenko, T. Farrelly, T. J. Osborne, R. Salzmann, D. Scheiermann, R. Wolf, Nature communications2020,11, 1 808

  53. [53]

    Cerezo, A

    M. Cerezo, A. Sone, T. Volkoff, L. Cincio, P. J. Coles,Nature communications2021,12, 1 1791

  54. [54]

    Schuld, N

    M. Schuld, N. Killoran,PRX Quantum2022,3030101

  55. [55]

    Pesah, M

    A. Pesah, M. Cerezo, S. Wang, T. Volkoff, A. T. Sornborger, P. J. Coles,Phys. Rev. X2021,11 041011

  56. [56]

    Wang,Science2003,299, 5613 1719–1722

    J. Wang,Science2003,299, 5613 1719–1722

  57. [57]

    Ramesh, N

    R. Ramesh, N. A. Spaldin,Nature Materials2007,6, 1 21–29

  58. [58]

    Bibes, A

    M. Bibes, A. Barthémy,Nature Materials2008,7, 6 425–426

  59. [59]

    Fiebig,Journal of Physics D: Applied Physics2005,38, 8 R123–R152

    M. Fiebig,Journal of Physics D: Applied Physics2005,38, 8 R123–R152

  60. [60]

    Hemberger, F

    J. Hemberger, F. Schrettle, A. Pimenov, P. Lunkenheimer, V. Y. Ivanov, A. A. Mukhin, A. M. Balbashov, A. Loidl,Phys. Rev. B2007,75035118

  61. [61]

    Stagraczyński, L

    S. Stagraczyński, L. Chotorlishvili, M. Schüler, M. Mierzejewski, J. Berakdar,Phys. Rev. B2017, 96054440

  62. [62]

    Khomeriki, L

    R. Khomeriki, L. Chotorlishvili, I. Tralle, J. Berakdar,Nano Letters2016,16, 11 7290–7294. 24 REFERENCES REFERENCES

  63. [63]

    Khomeriki, L

    R. Khomeriki, L. Chotorlishvili, B. A. Malomed, J. Berakdar,Phys. Rev. B2015,91041408

  64. [64]

    Cheong, M

    S.-W. Cheong, M. Mostovoy,Nature Materials2007,6, 1 13–20

  65. [65]

    Katsura, N

    H. Katsura, N. Nagaosa, A. V. Balatsky,Phys. Rev. Lett.2005,95057205

    2005

  66. [66]

    Mostovoy,Phys

    M. Mostovoy,Phys. Rev. Lett.2006,96067601

    2006

  67. [67]

    R. K. Shukla, L. Chotorlishvili, S. K. Mishra, F. Iemini,Phys. Rev. B2025,111024315

  68. [68]

    I. A. Sergienko, E. Dagotto,Phys. Rev. B2006,73094434

  69. [69]

    Tokura, S

    Y. Tokura, S. Seki, N. Nagaosa,Reports on Progress in Physics2014,77, 7 076501

  70. [70]

    S. V. Kolesnikov, E. S. Glazova, A. M. Saletsky,The European Physical Journal B2025,98, 8 172

  71. [71]

    Kolesnikov, E

    S. Kolesnikov, E. Glazova, A. Saletsky,Journal of Statistical Mechanics: Theory and Experiment 2025,2025, 5 053207

    2025

  72. [72]

    Moon, S.-M

    J.-H. Moon, S.-M. Seo, K.-J. Lee, K.-W. Kim, J. Ryu, H.-W. Lee, R. D. McMichael, M. D. Stiles, Phys. Rev. B2013,88184404

  73. [73]

    M. M. Wilde,Quantum information theory, Cambridge university press,2013

    2013

  74. [74]

    Kulig, P

    M. Kulig, P. Kurashvili, C. Jasiukiewicz, M. Inglot, S. Wolski, S. Stagraczyński, T. Masłowski, T. Szczepański, R. Stagraczyński, V. K. Dugaev, L. Chotorlishvili,Phys. Rev. B2023,108 134411

  75. [75]

    F. Ming, D. Wang, X.-G. Fan, W.-N. Shi, L. Ye, J.-L. Chen,Phys. Rev. A2020,102012206

  76. [76]

    Dolatkhah, S

    H. Dolatkhah, S. Haseli, S. Salimi, A. S. Khorashad,Phys. Rev. A2020,102052227

  77. [77]

    L. Wu, L. Ye, D. Wang,Phys. Rev. A2022,106062219

  78. [78]

    G. Zhu, A. Liu, L. Xiao, K. Wang, D. Qu, J. Li, C. Qiao, P. Xue,Phys. Rev. A2023,108 L050202

  79. [79]

    J. Lee, D. Kim, D.-H. Kim,Phys. Rev. B2019,99184202

  80. [80]

    Nakamura, E

    S. Nakamura, E. Iyoda, T. Deguchi, T. Sagawa,Phys. Rev. B2019,99224305

Showing first 80 references.