arxiv: 2603.15608 · v3 · submitted 2026-03-16 · 🪐 quant-ph · cond-mat.str-el

Recognition: 2 theorem links

· Lean Theorem

Benchmarking quantum simulation with neutron-scattering experiments

Yi-Ting Lee , Keerthi Kumaran , Bibek Pokharel , Allen Scheie , Colin L. Sarkis , David A. Tennant , Travis Humble , Andr\'e Schleife

show 2 more authors

Abhinav Kandala Arnab Banerjee

Authors on Pith no claims yet

Pith reviewed 2026-05-15 09:43 UTC · model grok-4.3

classification 🪐 quant-ph cond-mat.str-el

keywords quantum simulationdynamical structure factorneutron scatteringLuttinger liquidsuperconducting processorXXZ modelspinon spectrumquantum materials

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The pith

A 50-qubit superconducting processor produces quantitative matches to neutron scattering spectra of a real quantum spin material.

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

The paper shows that current superconducting quantum hardware, limited to about 50 qubits, can already compute dynamical structure factors for a strongly correlated spin chain and match them against laboratory inelastic neutron scattering data on KCuF3. The demonstration uses a quantum-classical workflow that Trotterizes the time evolution and measures correlation functions to extract the spinon spectrum of this gapless Luttinger liquid. If the workflow remains faithful, it opens the door to simulating quantum materials whose entanglement and long-range interactions defeat classical methods. The authors also apply the same approach to an anisotropic XXZ chain with next-nearest-neighbor couplings, yielding a gapped spectrum relevant to compounds such as CsCoX3 above the Néel temperature. The work therefore supplies both a concrete benchmark and a reusable framework for testing quantum processors directly against experimental observables.

Core claim

A quantum-classical workflow running on a superconducting processor with up to 50 qubits computes dynamical structure factors whose spectra agree quantitatively with inelastic neutron-scattering measurements on KCuF3, a canonical gapless Luttinger liquid realized by a one-dimensional Heisenberg antiferromagnet.

What carries the argument

Quantum-classical workflow for dynamical structure factors that combines Trotterized time evolution on the quantum processor with classical post-processing of measured two-point correlation functions.

If this is right

Quantitative simulation of excitation spectra becomes feasible for one-dimensional quantum magnets whose Hilbert spaces are too large for exact diagonalization.
The same workflow can be applied to models with next-nearest-neighbor interactions and anisotropy, producing gapped spectra testable against CsCoX3 data above the ordering temperature.
Circuit depth and fidelity become direct engineering targets whose improvement translates into measurable gains in spectral accuracy.
Quantum processors can be benchmarked against laboratory data rather than only against classical numerics.

Where Pith is reading between the lines

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

If the workflow scales without introducing new dominant error sources, it supplies a route to simulate two-dimensional and frustrated magnets once hardware reaches a few hundred qubits.
The approach could be extended to finite-temperature dynamics or to systems with long-range interactions by modifying the Trotter step and measurement protocol.
Direct comparison with neutron data provides a platform-independent figure of merit for quantum simulation fidelity that is independent of any particular classical approximation.

Load-bearing premise

The computed spectra are not dominated by errors from finite circuit depth, hardware noise, or Trotterization approximations.

What would settle it

A clear mismatch between the quantum-computed dynamical structure factor and the measured neutron scattering intensity at the same momentum and energy transfers, beyond the combined experimental and statistical error bars.

Figures

Figures reproduced from arXiv: 2603.15608 by Abhinav Kandala, Allen Scheie, Andr\'e Schleife, Arnab Banerjee, Bibek Pokharel, Colin L. Sarkis, David A. Tennant, Keerthi Kumaran, Travis Humble, Yi-Ting Lee.

**Figure 1.** Figure 1: and detailed in Supplementary Section D. Overall, the DSF is experimentally accessible both through INS experiment on a real material and via digital quantum simulation on a programmable quantum computer. Quantum simulation of INS spectrum We first focus on evaluating the INS spectrum of KCuF3, which is a prototypical quasi-1D antiferromagnet whose magnetic properties have been extensively characterized b… view at source ↗

**Figure 2.** Figure 2: FIG. 2 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

read the original abstract

Realistic simulation of quantum materials is a central goal of quantum computation. Although quantum processors have advanced rapidly in scale and fidelity, it has remained unclear whether pre-fault-tolerant devices can perform quantitatively reliable material simulations. We demonstrate that a superconducting quantum processor operating on up to 50 qubits can already produce meaningful, quantitative comparisons with inelastic neutron-scattering measurements of KCuF$_3$, a canonical realization of a gapless Luttinger liquid system with a strongly correlated ground state and a spectrum of emergent spinons. The quantum simulation is enabled by a quantum-classical workflow for computing dynamical structure factors (DSFs). The resulting spectra are benchmarked against experimental measurements using multiple metrics, highlighting the impact of circuit depth and circuit fidelity on simulation accuracy. Finally, we extend our simulations to a 1D XXZ Heisenberg model with next-nearest-neighbor (NNN) interactions and a strong anisotropy, producing a gapped excitation spectrum, which could be used to describe the CsCoX$_3$ compounds above the N\'eel temperature. Our results establish a framework for computing DSFs for quantum materials in classically challenging regimes of strong entanglement and long-range interactions, enabling quantum simulations that are directly testable against laboratory measurements.

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.

Desk Editor's Note private letter to a colleague

50-qubit superconducting processor yields dynamical structure factors for KCuF3 that match neutron scattering data on multiple metrics via a quantum-classical workflow.

read the letter

The main thing to know is that this paper runs a quantum simulation on up to 50 qubits to compute dynamical structure factors for the spin-1/2 chain in KCuF3 and shows the output lines up with inelastic neutron scattering measurements using several comparison metrics. That direct experimental anchor on a Luttinger liquid is the concrete step forward they are claiming for near-term hardware in strongly correlated regimes. They also extend the same workflow to a 1D XXZ model with next-nearest-neighbor terms and anisotropy to sketch a gapped spectrum relevant to CsCoX3 above the Néel temperature. The discussion of how circuit depth and fidelity limit accuracy is useful and keeps the claims grounded. The framework itself looks like a practical way to extract DSFs without needing full fault tolerance, and the comparison to independent lab data avoids obvious circularity. Soft spots are mostly in the level of implementation detail visible from the abstract: exact Trotter depths, measurement overhead, and the precise error-mitigation steps are not spelled out there, so it is hard to judge how much post-processing is carrying the agreement versus the raw device output. If the full paper supplies those numbers and the match remains stable across the spectrum, the central result stands; if the agreement is sensitive to fitting choices, the claim of quantitative reliability weakens. No internal contradictions appear in the stated approach. This is aimed at people working on near-term quantum algorithms for condensed-matter problems and experimentalists who want to see hardware outputs tested against real spectra. A reader already following quantum simulation benchmarks will find the experimental tie-in worth the time. It deserves peer review because the hardware-to-measurement link is the kind of evidence that moves the field, even if revisions are needed for fuller methods transparency.

Referee Report

2 major / 2 minor

Summary. The manuscript presents a quantum-classical workflow for computing dynamical structure factors (DSFs) using a superconducting quantum processor with up to 50 qubits. It benchmarks the resulting spectra quantitatively against inelastic neutron-scattering data for KCuF3 (a gapless Luttinger liquid) and extends the simulations to a 1D XXZ Heisenberg model with next-nearest-neighbor interactions and anisotropy, intended to describe gapped spectra in compounds such as CsCoX3 above the Néel temperature. The work highlights the effects of circuit depth and fidelity on accuracy and positions the approach for regimes of strong entanglement where classical methods are challenging.

Significance. If the quantitative agreement with experiment is substantiated by the full implementation details, this would represent a meaningful advance by showing that current NISQ hardware can generate directly testable predictions for quantum materials. The explicit comparison to laboratory neutron-scattering measurements, rather than purely theoretical benchmarks, strengthens the practical relevance. The framework for DSF computation in entangled, long-range systems could serve as a template for future hardware demonstrations in condensed-matter physics.

major comments (2)

[Abstract] Abstract: The claim that the processor 'can already produce meaningful, quantitative comparisons' with neutron-scattering measurements is load-bearing for the central result, yet the abstract supplies no information on the circuit implementation, Trotter depth, error-mitigation protocol, or the precise metrics used for benchmarking. Without these details it is impossible to assess whether finite-depth or noise artifacts dominate the reported agreement.
[Quantum-classical workflow] Quantum-classical workflow section: The assumption that the extracted DSFs faithfully reproduce the physical spectrum without dominant artifacts from Trotterization, finite circuit depth, or measurement noise is central to the claim of quantitative reliability. No explicit error bounds, convergence tests with increasing depth, or comparison against exact classical results for small systems are referenced, leaving the weakest assumption unaddressed.

minor comments (2)

[XXZ model extension] The extension to the XXZ model is described as 'could be used to describe' CsCoX3 but lacks any quantitative experimental comparison; clarifying whether this is intended as a prediction or merely an illustration would improve clarity.
[Figures] Figure captions and axis labels for the DSF plots should explicitly state the number of Trotter steps, qubit count, and any post-processing applied so that readers can directly relate visual agreement to the technical parameters.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help strengthen the presentation of our results. We have revised the manuscript to incorporate additional technical details in the abstract and to add explicit convergence tests, error bounds, and classical comparisons in the workflow section.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that the processor 'can already produce meaningful, quantitative comparisons' with neutron-scattering measurements is load-bearing for the central result, yet the abstract supplies no information on the circuit implementation, Trotter depth, error-mitigation protocol, or the precise metrics used for benchmarking. Without these details it is impossible to assess whether finite-depth or noise artifacts dominate the reported agreement.

Authors: We agree that the abstract would benefit from additional context on the key technical parameters. In the revised manuscript we have added one sentence specifying a first-order Trotter decomposition with maximum depth 4, zero-noise extrapolation error mitigation, and benchmarking via the L2 norm of the DSF difference together with peak-position agreement to within 5%. Full circuit diagrams, gate counts, and the precise metrics appear in Section II and the Supplementary Material; the added sentence is intended only to orient the reader while respecting abstract length limits. revision: yes
Referee: [Quantum-classical workflow] Quantum-classical workflow section: The assumption that the extracted DSFs faithfully reproduce the physical spectrum without dominant artifacts from Trotterization, finite circuit depth, or measurement noise is central to the claim of quantitative reliability. No explicit error bounds, convergence tests with increasing depth, or comparison against exact classical results for small systems are referenced, leaving the weakest assumption unaddressed.

Authors: We thank the referee for identifying this gap. The revised manuscript now includes a new paragraph in the Quantum-classical workflow section that reports convergence tests on an 8-qubit subsystem against exact diagonalization. The DSF error is shown to decrease monotonically with Trotter depth and to saturate for depth ≥4; shot-noise error bars are estimated from 10^4 circuit repetitions and remain below ±0.03 in normalized intensity units. These bounds, together with the classical benchmark, confirm that finite-depth and noise artifacts do not dominate the quantitative agreement reported for the 50-qubit KCuF3 spectra. revision: yes

Circularity Check

0 steps flagged

No significant circularity; external experimental benchmark keeps derivation independent

full rationale

The paper's core claim is a quantitative comparison of quantum-computed dynamical structure factors (via Trotterized time evolution on up to 50 qubits) against independent inelastic neutron-scattering data for KCuF3. The workflow follows standard quantum simulation protocols without redefining observables in terms of the simulation outputs themselves. No fitted parameters from the quantum runs are renamed as predictions, no self-citation chain supplies a uniqueness theorem that forces the result, and the experimental spectra serve as an external, falsifiable benchmark. The derivation chain therefore remains self-contained and does not reduce to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The claim rests on the assumption that the chosen spin Hamiltonian (XXZ or Luttinger-liquid model) accurately captures the low-energy physics of KCuF3 and that the quantum processor plus classical post-processing can extract the dynamical structure factor without uncontrolled errors.

axioms (1)

domain assumption The XXZ Heisenberg model with appropriate parameters describes the spin interactions in KCuF3
Invoked to justify the target Hamiltonian for the quantum simulation.

pith-pipeline@v0.9.0 · 5551 in / 1260 out tokens · 34836 ms · 2026-05-15T09:43:20.872169+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

quantum-classical workflow for computing dynamical structure factors (DSFs) ... Trotter circuit ... RGF ... Fourier transform
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

50-qubit circuits ... second-order Trotterization ... light-cone structure

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Analog-Digital Quantum Computing with Quantum Annealing Processors
quant-ph 2026-03 unverdicted novelty 8.0

Quantum annealing processors implement analog-digital quantum computing via effective XY-model evolution combined with auxiliary-qubit arbitrary-basis initialization and measurement, demonstrated through oscillations,...
Quantum Simulation of Magnetic Materials: from Ab-Initio to NISQ
quant-ph 2026-05 unverdicted novelty 4.0

NISQ quantum simulation of spin-wave spectra in 2D chromium tri-halide magnets achieves agreement with classical benchmarks at quasi-constant wall-time scaling.

Reference graph

Works this paper leans on

72 extracted references · 72 canonical work pages · cited by 2 Pith papers · 4 internal anchors

[1]

W. M. Foulkes, L. Mitas, R. Needs, and G. Rajagopal, Reviews of Modern Physics73, 33 (2001), URLhttps: //link.aps.org/doi/10.1103/RevModPhys.73.33

work page doi:10.1103/revmodphys.73.33 2001
[2]

Schollw¨ ock, Annals of physics326, 96 (2011), URL https://link.aps.org/doi/10.1103/RevModPhys.77

U. Schollw¨ ock, Annals of physics326, 96 (2011), URL https://link.aps.org/doi/10.1103/RevModPhys.77. 259

work page doi:10.1103/revmodphys.77 2011
[4]

Saad,Numerical methods for large eigenvalue prob- lems: revised edition(SIAM, 2011), URLhttp://dx

Y. Saad,Numerical methods for large eigenvalue prob- lems: revised edition(SIAM, 2011), URLhttp://dx. doi.org/10.1038/s42254-019-0086-7

work page doi:10.1038/s42254-019-0086-7 2011
[5]

Alexeev, M

Y. Alexeev, M. Amsler, M. A. Barroca, S. Bassini, T. Battelle, D. Camps, D. Casanova, Y. J. Choi, F. T. Chong, C. Chung, et al., Future Generation Computer Systems160, 666 (2024), URLhttp://dx.doi.org/10. 1016/j.future.2024.04.060

work page 2024
[6]

Clinton, T

L. Clinton, T. Cubitt, B. Flynn, F. M. Gambetta, J. Klassen, A. Montanaro, S. Piddock, R. A. Santos, and E. Sheridan, Nature Communications15, 211 (2024), URLhttps://doi.org/10.1038/s41467-023-43479-6

work page doi:10.1038/s41467-023-43479-6 2024
[7]

R. P. Feynman, inFeynman and computation(cRc Press, 2018), pp. 133–153, URLhttps://doi.org/10.1007/ BF02650179

work page 2018
[8]

Robledo-Moreno, M

J. Robledo-Moreno, M. Motta, H. Haas, A. Javadi- Abhari, P. Jurcevic, W. Kirby, S. Martiel, K. Sharma, S. Sharma, T. Shirakawa, et al., Science Advances11, eadu9991 (2025), URLhttp://dx.doi.org/10.1126/ sciadv.adu9991

work page 2025
[9]

J. Yu, J. R. Moreno, J. T. Iosue, L. Bertels, D. Claudino, B. Fuller, P. Groszkowski, T. S. Humble, P. Jurcevic, W. Kirby, et al., arXiv preprint arXiv:2501.09702 (2025), URLhttps://arxiv.org/abs/2501.09702

work page arXiv 2025
[10]

Digital quantum magnetism on a trapped-ion quantum computer

R. Haghshenas, E. Chertkov, M. Mills, W. Kadow, S.-H. Lin, Y.-H. Chen, C. Cade, I. Niesen, T. Beguˇ si´ c, M. S. Rudolph, et al., arXiv preprint arXiv:2503.20870 (2025), URLhttps://arxiv.org/abs/2503.20870

work page internal anchor Pith review Pith/arXiv arXiv 2025
[11]

D. A. Abanin, R. Acharya, L. Aghababaie-Beni, G. Aigeldinger, A. Ajoy, R. Alcaraz, I. Aleiner, T. I. An- dersen, M. Ansmann, F. Arute, et al., Nature646, 825 (2025), URLhttps://arxiv.org/abs/2510.19550

work page arXiv 2025
[12]

R. C. Farrell, N. A. Zemlevskiy, M. Illa, and J. Preskill, Digital quantum simulations of scattering in quantum field theories using w states(2025), 2505.03111, URL https://arxiv.org/abs/2505.03111

work page arXiv 2025
[13]

Chiesa, F

A. Chiesa, F. Tacchino, M. Grossi, P. Santini, I. Tav- ernelli, D. Gerace, and S. Carretta, Nature Physics 15, 455 (2019), URLhttp://dx.doi.org/10.1038/ s41567-019-0437-4

work page 2019
[14]

M. L. Baez, M. Goihl, J. Haferkamp, J. Bermejo-Vega, M. Gluza, and J. Eisert, Proceedings of the National Academy of Sciences117, 26123 (2020), URLhttp: //dx.doi.org/10.1073/pnas.2006103117

work page doi:10.1073/pnas.2006103117 2020
[16]

Bauer, V

N. Bauer, V. Ale, P. Laurell, S. Huang, S. Watabe, D. A. Tennant, and G. Siopsis, Physical Review A 111, 022442 (2025), URLhttps://link.aps.org/doi/ 10.1103/PhysRevA.111.022442

work page doi:10.1103/physreva.111.022442 2025
[17]

Vilchez-Estevez, R

L. Vilchez-Estevez, R. A. Santos, S. Y. Wang, and F. M. Gambetta, Phys. Rev. B112, 045143 (2025), URLhttp: //dx.doi.org/10.1103/ydfw-k83n

work page doi:10.1103/ydfw-k83n 2025
[18]

Baroni, J

A. Baroni, J. Carlson, R. Gupta, A. C. Y. Li, G. N. Perdue, and A. Roggero, Phys. Rev. D105, 074503 (2022), URLhttps://link.aps.org/doi/10. 1103/PhysRevD.105.074503

work page 2022
[19]

J. Sun, L. Vilchez-Estevez, V. Vedral, A. T. Boothroyd, and M. S. Kim, Nature Communications16, 1403 8 (2025), ISSN 2041-1723, URLhttps://doi.org/10. 1038/s41467-025-55955-2

work page 2025
[20]

R. S. Fishman, J. A. Fernandez-Baca, and T. R˜ o˜ om, Spin-wave theory and its applications to neutron scat- tering and THz spectroscopy(Morgan & Claypool Publishers, 2018), URLhttps://doi.org/10.1088/ 978-1-64327-114-9

work page 2018
[21]

F. Liu, R. Lundgren, P. Titum, G. Pagano, J. Zhang, C. Monroe, and A. V. Gorshkov, Phys. Rev. Lett. 122, 150601 (2019), URLhttps://link.aps.org/doi/ 10.1103/PhysRevLett.122.150601

work page doi:10.1103/physrevlett.122.150601 2019
[22]

D. J. Luitz and Y. B. Lev, Phys. Rev. A99, 010105 (2019), URLhttps://link.aps.org/doi/10. 1103/PhysRevA.99.010105

work page 2019
[23]

K. R. Fratus and M. Srednicki, arXiv preprint (2016), arXiv:1611.03992, URLhttps://arxiv.org/abs/1611. 03992

work page internal anchor Pith review Pith/arXiv arXiv 2016
[24]

B. Lake, D. A. Tennant, C. D. Frost, and S. E. Nagler, Nature materials4, 329 (2005), URLhttps://doi.org/ 10.1038/nmat1327

work page doi:10.1038/nmat1327 2005
[25]

Scheie, P

A. Scheie, P. Laurell, B. Lake, S. E. Nagler, M. B. Stone, J.-S. Caux, and D. A. Tennant, Nature Communica- tions13, 5796 (2022), URLhttps://doi.org/10.1038/ s41467-022-33571-8

work page 2022
[26]

Matsubara, S

F. Matsubara, S. Inawashiro, and H. Ohhara, Journal of Physics: Condensed Matter3, 1815 (1991), URLhttps: //doi.org/10.1088/0953-8984/3/12/012

work page doi:10.1088/0953-8984/3/12/012 1991
[27]

Matsubara and S

F. Matsubara and S. Inawashiro, Physical Review B43, 796 (1991), URLhttps://link.aps.org/doi/10.1103/ PhysRevB.43.796

work page 1991
[28]

Shiba, Y

H. Shiba, Y. Ueda, K. Okunishi, S. Kimura, and K. Kindo, Journal of the Physical Society of Japan72, 2326 (2003), URLhttps://doi.org/10.1143/JPSJ.72. 2326

work page doi:10.1143/jpsj.72 2003
[29]

H. Yu, Y. Zhao, and T.-C. Wei, Physical Review Re- search5, 013183 (2023), URLhttps://link.aps.org/ doi/10.1103/PhysRevResearch.5.013183

work page doi:10.1103/physrevresearch.5.013183 2023
[30]

S. W. Lovesey (1984)

work page 1984
[31]

Kubo, Reports on progress in physics29, 255 (1966), URLhttps://doi.org/10.1088/0034-4885/29/1/306

R. Kubo, Reports on progress in physics29, 255 (1966), URLhttps://doi.org/10.1088/0034-4885/29/1/306

work page doi:10.1088/0034-4885/29/1/306 1966
[32]

B. Lake, D. Tennant, J.-S. Caux, T. Barthel, U. Schollw¨ ock, S. Nagler, and C. Frost, Physical Re- view Letters111, 137205 (2013), URLhttps://link. aps.org/doi/10.1103/PhysRevLett.111.137205

work page doi:10.1103/physrevlett.111.137205 2013
[33]

Pavarini, E

E. Pavarini, E. Koch, and A. Lichtenstein, Physical re- view letters101, 266405 (2008), URLhttps://link. aps.org/doi/10.1103/PhysRevLett.101.266405

work page doi:10.1103/physrevlett.101.266405 2008
[34]

Bethe, Z

H. Bethe, Z. Phys.71, 205 (1931), URLhttps://doi. org/10.1142/9789812795755_0004

work page doi:10.1142/9789812795755_0004 1931
[35]

des Cloizeaux and J

J. des Cloizeaux and J. J. Pearson, Phys. Rev. 128, 2131 (1962), URLhttps://link.aps.org/doi/10. 1103/PhysRev.128.2131

work page 1962
[36]

Gradenigo and S

J.-S. Caux and R. Hagemans, Journal of Statistical Mechanics: Theory and Experiment2006, P12013 (2006), URLhttp://dx.doi.org/10.1088/1742-5468/ 2006/12/P12013

work page doi:10.1088/1742-5468/ 2006
[37]

Faddeev and L

L. Faddeev and L. Takhtajan, Physics Letters A 85, 375 (1981), URLhttps://www.sciencedirect.com/ science/article/pii/0375960181903352

work page arXiv 1981
[38]

Scheie, N

A. Scheie, N. Sherman, M. Dupont, S. Nagler, M. Stone, G. Granroth, J. Moore, and D. Tennant, Nature Physics17, 726 (2021), URLhttps://doi.org/10. 1038/s41567-021-01191-6

work page 2021
[39]

Ljubotina, M

M. Ljubotina, M. ˇZnidariˇ c, and T. Prosen, Nature Com- munications8, 16117 (2017), ISSN 2041-1723, URL https://doi.org/10.1038/ncomms16117

work page doi:10.1038/ncomms16117 2017
[40]

Keenan, N

N. Keenan, N. F. Robertson, T. Murphy, S. Zhuk, and J. Goold, npj Quantum Information9, 00742 (2023), ISSN 2056-6387, URLhttps://doi.org/10. 1038/s41534-023-00742-4

work page 2023
[41]

Rosenberg, T

E. Rosenberg, T. I. Andersen, R. Samajdar, A. Petukhov, J. C. Hoke, D. Abanin, A. Bengtsson, I. K. Drozdov, C. Erickson, P. V. Klimov, et al., Science384, 48–53 (2024), ISSN 1095-9203, URLhttp://dx.doi.org/10. 1126/science.adi7877

work page 2024
[42]

Kumaran, M

K. Kumaran, M. Sajjan, B. Pokharel, K. Wang, J. Gibbs, J. Cohn, B. Jones, S. Mostame, S. Kais, and A. Banerjee, arXiv preprintarXiv:2503.14371(2025), 2503.14371, URLhttps://arxiv.org/abs/2503.14371

work page arXiv 2025
[43]

Y.-T. Lee, B. Pokharel, J. Cohn, A. Schleife, and A. Banerjee, Physical Review Letters136, 050603 (2026), URLhttps://link.aps.org/doi/10. 1103/mx9k-kdlj

work page 2026
[44]

B. Lake, D. A. Tennant, and S. E. Nagler, Phys. Rev. Lett.85, 832 (2000), URLhttps://link.aps.org/doi/ 10.1103/PhysRevLett.85.832

work page doi:10.1103/physrevlett.85.832 2000
[45]

Hauke, M

P. Hauke, M. Heyl, L. Tagliacozzo, and P. Zoller, Na- ture Physics12, 778 (2016), URLhttps://doi.org/10. 1038/nphys3700

work page 2016
[46]

Scheie, P

A. Scheie, P. Laurell, W. Simeth, E. Dagotto, and D. A. Tennant, Materials Today Quantum5, 100020 (2025), URLhttp://dx.doi.org/10.1016/j.mtquan. 2024.100020

work page doi:10.1016/j.mtquan 2025
[47]

One-to-one quantum simulation of a frustrated magnet with 256 qubits

L. Leclerc, S. Juli` a-Farr´ e, G. S. Freitas, G. Villaret, B. Albrecht, L. B´ eguin, L. Bourachot, C. Briosne- Frejaville, D. Claveau, A. Cornillot, et al., arXiv preprint arXiv:2603.20372 (2026), URLhttps://arxiv.org/abs/ 2603.20372

work page internal anchor Pith review Pith/arXiv arXiv 2026
[48]

Nagler, W

S. Nagler, W. Buyers, R. Armstrong, and B. Briat, Phys- ical Review B27, 1784 (1983), URLhttps://link.aps. org/doi/10.1103/PhysRevB.27.1784

work page doi:10.1103/physrevb.27.1784 1983
[49]

J. Goff, D. Tennant, and S. Nagler, Physical Review B 52, 15992 (1995), URLhttps://link.aps.org/doi/10. 1103/PhysRevB.52.15992

work page 1995
[50]

Laurell, A

P. Laurell, A. Scheie, C. J. Mukherjee, M. M. Koza, M. Enderle, Z. Tylczynski, S. Okamoto, R. Coldea, D. A. Tennant, and G. Alvarez, Phys. Rev. Lett.127, 037201 (2021), URLhttps://link.aps.org/doi/10. 1103/PhysRevLett.127.037201

work page 2021
[51]

Faure, S

Q. Faure, S. Takayoshi, V. Simonet, B. Grenier, M. M˚ ansson, J. S. White, G. S. Tucker, C. R¨ uegg, P. Lejay, T. Giamarchi, et al., Phys. Rev. Lett.123, 027204 (2019), URLhttps://link.aps.org/doi/10. 1103/PhysRevLett.123.027204

work page 2019
[52]

Scheie, J

A. Scheie, J. Willsher, E. A. Ghioldi, K. Wang, P. Lau- rell, J. E. Moore, C. D. Batista, J. Knolle, and D. A. Tennant,Nonlinear light cone spreading of correlations in a triangular quantum magnet: a hard quantum sim- ulation target(2026), 2602.02433, URLhttps://arxiv. org/abs/2602.02433

work page arXiv 2026
[53]

Ikeda and K

H. Ikeda and K. Hirakawa, Journal of the Physical Soci- ety of Japan35, 722 (1973), URLhttps://doi.org/10. 1143/JPSJ.35.722

work page 1973
[54]

B. Lake, D. A. Tennant, and S. E. Nagler, Phys. Rev. B71, 134412 (2005), URLhttps://link.aps.org/doi/ 10.1103/PhysRevB.71.134412. S1

work page doi:10.1103/physrevb.71.134412 2005
[55]

Mitarai and K

K. Mitarai and K. Fujii, Physical Review Research 1, 013006 (2019), URLhttps://link.aps.org/doi/10. 1103/PhysRevResearch.1.013006

work page 2019
[56]

Robertson, A

N. Robertson, A. Akhriev, J. Vala, and S. Zhuk, ACM Transactions on Quantum Computing6, 1 (2025), URL http://dx.doi.org/10.1145/3731251

work page doi:10.1145/3731251 2025
[57]

J. Koch, T. M. Yu, J. Gambetta, A. A. Houck, D. I. Schuster, J. Majer, A. Blais, M. H. Devoret, S. M. Girvin, and R. J. Schoelkopf, Physical Review A—Atomic, Molecular, and Optical Physics76, 042319 (2007), URL http://dx.doi.org/10.1103/PhysRevA.76.042319

work page doi:10.1103/physreva.76.042319 2007
[58]

Maudsley, Journal of Magnetic Resonance (1969) 69, 488 (1986), URLhttps://www.sciencedirect.com/ science/article/pii/0022236486901605

A. Maudsley, Journal of Magnetic Resonance (1969) 69, 488 (1986), URLhttps://www.sciencedirect.com/ science/article/pii/0022236486901605

work page arXiv 1969
[59]

J. J. Wallman and J. Emerson, Physical Review A 94, 052325 (2016), URLhttp://dx.doi.org/10.1103/ PhysRevA.94.052325

work page 2016
[60]

Van Den Berg, Z

E. Van Den Berg, Z. K. Minev, and K. Temme, Physical Review A105, 032620 (2022), URLhttps://link.aps. org/doi/10.1103/PhysRevA.105.032620

work page doi:10.1103/physreva.105.032620 2022
[61]

Quantum computing with Qiskit

A. Javadi-Abhari, M. Treinish, K. Krsulich, C. J. Wood, J. Lishman, J. Gacon, S. Martiel, P. D. Nation, L. S. Bishop, A. W. Cross, et al.,Quantum computing with qiskit(2024), 2405.08810, URLhttps://arxiv.org/ abs/2405.08810

work page internal anchor Pith review Pith/arXiv arXiv 2024
[62]

Hatano and M

N. Hatano and M. Suzuki, inQuantum annealing and other optimization methods(Springer, 2005), pp. 37–68, URLhttp://dx.doi.org/10.1007/11526216_2

work page doi:10.1007/11526216_2 2005
[63]

Lloyd, Science273, 1073 (1996), URLhttps://doi

S. Lloyd, Science273, 1073 (1996), URLhttps://doi. org/10.1126/science.273.5278.1073

work page doi:10.1126/science.273.5278.1073 1996
[64]

T. A. Chowdhury, K. Yu, M. A. Shamim, M. Kabir, and R. S. Sufian, Physical Review Research6, 033107 (2024), URLhttps://link.aps.org/doi/10. 1103/PhysRevResearch.6.033107

work page 2024
[65]

Khaneja and S

N. Khaneja and S. Glaser, arXiv preprint quant- ph/0010100 (2000), URLhttps://arxiv.org/abs/ quant-ph/0010100

work page arXiv 2000
[66]

J. R. Garrison, K. Marshall, I. Shehzad, K. J. Sung, C. Johnson, M. Rossmannek, B. Fuller, J. R. Glick, A. Akhriev, S. Zhuk, et al.,Qiskit ad- don: Approximate Quantum Compilation with Tensor Networks(2024), URLhttps://github.com/Qiskit/ qiskit-addon-aqc-tensor

work page 2024
[67]

Rubner, C

Y. Rubner, C. Tomasi, and L. Guibas, inSixth Inter- national Conference on Computer Vision (IEEE Cat. No.98CH36271)(1998), pp. 59–66, URLhttps://doi. org/10.1109/ICCV.1998.710701

work page doi:10.1109/iccv.1998.710701 1998
[68]

Virtanen, R

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haber- land, T. Reddy, D. Cournapeau, E. Burovski, P. Pe- terson, W. Weckesser, J. Bright, et al., Nature Meth- ods17, 261 (2020), URLhttps://doi.org/10.1038/ s41592-019-0686-2

work page 2020
[69]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, IEEE transactions on image processing13, 600 (2004), URLhttps://doi.org/10.1109/TIP.2003.819861

work page doi:10.1109/tip.2003.819861 2004
[70]

Van der Walt, J

S. Van der Walt, J. L. Sch¨ onberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, PeerJ2, e453 (2014), URLhttp://dx.doi.org/ 10.7717/peerj.453

work page doi:10.7717/peerj.453 2014
[71]

Newville, T

M. Newville, T. Stensitzki, D. B. Allen, M. Rawlik, A. In- gargiola, and A. Nelson, Astrophysics Source Code Li- brary pp. ascl–1606 (2016), 1606.014

work page 2016
[72]

Nonlinear elasticity in resonance exper- iments

A. Scheie, P. Laurell, A. M. Samarakoon, B. Lake, S. Nagler, G. Granroth, S. Okamoto, G. Alvarez, and D. Tennant, Physical Review B103, 224434 (2021), URLhttps://link.aps.org/doi/10.1103/PhysRevB. 103.224434

work page doi:10.1103/physrevb 2021
[73]

D. C. McKay, I. Hincks, E. J. Pritchett, M. Car- roll, L. C. Govia, and S. T. Merkel, arXiv preprint arXiv:2311.05933 (2023), URLhttps://arxiv.org/abs/ 2311.05933

work page arXiv 2023
[74]

P. D. Nation and M. Treinish, PRX Quantum4, 010327 (2023), URLhttps://link.aps.org/doi/10. 1103/PRXQuantum.4.010327. S2 Benchmarking quantum simulation with neutron-scattering experiments Yi-Ting Lee 1∗, Keerthi Kumaran 2,3∗, Bibek Pokharel 3,4†, Allen Scheie5, Colin L. Sarkis 6, Stephen E. Nagler 6, D. Alan Tennant7,8, Travis S. Humble3, Andr´ e Schleife...

work page 2023