Quantum simulation of 2D antiferromagnets with hundreds of Rydberg atoms
Open accessAbstract
Quantum simulation using synthetic systems is a promising route to solve outstanding quantum many-body problems in regimes where other approaches, including numerical ones, fail 1. Many platforms are being developed towards this goal, in particular based on trapped ions 2-4, superconducting circuits 5-7, neutral atoms 8-11 or molecules 12,13. All of these platforms face two key challenges: scaling up the ensemble size while retaining high-quality control over the parameters, and validating the outputs for these large systems. Here we use programmable arrays of individual atoms trapped in optical tweezers, with interactions controlled by laser excitation to Rydberg states 11, to implement an iconic many-body problem-the antiferromagnetic two-dimensional transverse-field Ising model. We push this platform to a regime with up to 196 atoms manipulated with high fidelity and probe the antiferromagnetic order by dynamically tuning the parameters of the Hamiltonian. We illustrate the versatility of our platform by exploring various system sizes on two qualitatively different geometries-square and triangular arrays. We obtain good agreement with numerical calculations up to a computationally feasible size (approximately 100 particles). This work demonstrates that our platform can be readily used to address open questions in many-body physics.
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Tensor networks are considered the state of the art for simulation of quantum states and dynamics in low-dimensional quantum systems.
“Tensor networks are considered the state of the art for simulation of quantum states and dynamics in low-dimensional quantum systems 27,[41][42][43][44][45]48 .”
From Beyond-classical computation in quantum simulation (2025) · cites this paper as [27]
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Georgescu, I. M., Ashhab, S., & Nori, F. (2014) · Reviews of Modern Physics · doi:10.1103/revmodphys.86.153 · Cited by 2,932 · see citing works
Probing many-body dynamics on a 51-atom quantum simulator
Bernien, H., Schwartz, S., Keesling, A., Levine, H., Omran, A., Pichler, H., Choi, S., Zibrov, A. S., Endres, M., Greiner, M., et al. (2017) · Nature · doi:10.1038/nature24622 · Cited by 2,460 · see citing works
Quantum simulations with ultracold quantum gases
Bloch, I., Dalibard, J., & Nascimbène, S. (2012) · Nature Physics · doi:10.1038/nphys2259 · Cited by 2,339 · see citing works
Quantum simulations with trapped ions
Blatt, R., & Roos, C. F. (2012) · Nature Physics · doi:10.1038/nphys2252 · Cited by 1,629 · see citing works
Superconducting Qubits: Current State of Play
Kjaergaard, M., Schwartz, M. E., Braumüller, J., Krantz, P., Wang, J. I.-J., Gustavsson, S., & Oliver, W. D. (2020) · Annual Review of Condensed Matter Physics · doi:10.1146/annurev-conmatphys-031119-050605 · Cited by 1,544 · see citing works
Quantum simulations with ultracold atoms in optical lattices
Gross, C., & Bloch, I. (2017) · Science · doi:10.1126/science.aal3837 · Cited by 1,527 · see citing works
Quantum phases of matter on a 256-atom programmable quantum simulator
Ebadi, S., Wang, T. T., Levine, H., Keesling, A., Semeghini, G., Omran, A., Bluvstein, D., Samajdar, R., Pichler, H., Ho, W. W., et al. (2021) · Nature · doi:10.1038/s41586-021-03582-4 · Cited by 995 · see citing works
Programmable Quantum Simulations of Spin Systems with Trapped Ions
Monroe, C., Campbell, W. C., Duan, L. M., Gong, Z. X., Gorshkov, A. V., Hess, P., Islam, R., Kim, K., Linke, N., Pagano, G., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.07845 · Cited by 806
Unifying time evolution and optimization with matrix product states
Haegeman, J., Lubich, C., Oseledets, I., Vandereycken, B., & Verstraete, F. (2016) · Physical Review B · doi:10.1103/physrevb.94.165116 · Cited by 804 · see citing works
Quantum computing with neutral atoms
Henriet, L., Beguin, L., Signoles, A., Lahaye, T., Browaeys, A., Reymond, G.-O., & Jurczak, C. (2020) · Quantum · doi:10.22331/q-2020-09-21-327 · Cited by 550 · see citing works
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Logical quantum processor based on reconfigurable atom arrays
Bluvstein, D., Evered, S. J., Geim, A. A., Li, S. H., Zhou, H., Manovitz, T., Ebadi, S., Cain, M., Kalinowski, M., Hangleiter, D., et al. (2024) · Nature · doi:10.1038/s41586-023-06927-3 · Cited by 1,119 · see citing works
“This process realizes a high-fidelity, fault-tolerant transversal CNOT in a single parallel step.”
BackgroundQuantum phases of matter on a 256-atom programmable quantum simulator
Ebadi, S., Wang, T. T., Levine, H., Keesling, A., Semeghini, G., Omran, A., Bluvstein, D., Samajdar, R., Pichler, H., Ho, W. W., et al. (2021) · Nature · doi:10.1038/s41586-021-03582-4 · Cited by 995 · see citing works
Multi-qubit entanglement and algorithms on a neutral-atom quantum computer
Graham, T. M., Song, Y., Scott, J., Poole, C., Phuttitarn, L., Jooya, K., Eichler, P., Jiang, X., Marra, A., Grinkemeyer, B., et al. (2022) · Nature · doi:10.1038/s41586-022-04603-6 · Cited by 518 · see citing works
High-fidelity parallel entangling gates on a neutral-atom quantum computer
Evered, S. J., Bluvstein, D., Kalinowski, M., Ebadi, S., Manovitz, T., Zhou, H., Li, S. H., Geim, A. A., Wang, T. T., Maskara, N., et al. (2023) · Nature · doi:10.1038/s41586-023-06481-y · Cited by 522 · see citing works
Quantum optimization of maximum independent set using Rydberg atom arrays
Ebadi, S., Keesling, A., Cain, M., Wang, T. T., Levine, H., Bluvstein, D., Semeghini, G., Omran, A., Liu, J.-G., Samajdar, R., et al. (2022) · Science · doi:10.1126/science.abo6587 · Cited by 430 · see citing works
Quantum annealing for industry applications: introduction and review
Yarkoni, S., Raponi, E., Bäck, T., & Schmitt, S. (2022) · Reports on Progress in Physics · doi:10.1088/1361-6633/ac8c54 · Cited by 341 · see citing works
High-fidelity gates and mid-circuit erasure conversion in an atomic qubit
Ma, S., Liu, G., Peng, P., Zhang, B., Jandura, S., Claes, J., Burgers, A. P., Pupillo, G., Puri, S., & Thompson, J. D. (2023) · Nature · doi:10.1038/s41586-023-06438-1 · Cited by 228 · see citing works
Beyond-classical computation in quantum simulation
King, A. D., Nocera, A., Rams, M. M., Dziarmaga, J., Wiersema, R., Bernoudy, W., Raymond, J., Kaushal, N., Heinsdorf, N., Harris, R., et al. (2025) · Science · doi:10.1126/science.ado6285 · Cited by 138 · see citing works
Benchmarking highly entangled states on a 60-atom analogue quantum simulator
Shaw, A. L., Chen, Z., Choi, J., Mark, D. K., Scholl, P., Finkelstein, R., Elben, A., Choi, S., & Endres, M. (2024) · Nature · doi:10.1038/s41586-024-07173-x · Cited by 64 · see citing works
Self-citationLimitations of Linear Cross-Entropy as a Measure for Quantum Advantage
Gao, X., Kalinowski, M., Chou, C.-N., Lukin, M. D., Barak, B., & Choi, S. (2021) · arXiv · doi:10.48550/arxiv.2112.01657 · Cited by 54 · see citing works
Emergence of Gapless Quantum Spin Liquid from Deconfined Quantum Critical Point
Liu, W.-Y., Hasik, J., Gong, S.-S., Poilblanc, D., Chen, W.-Q., & Gu, Z.-C. (2022) · Physical Review X · doi:10.1103/physrevx.12.031039 · Cited by 53 · see citing works
Dalyac, C., Henriet, L., Jeandel, E., Lechner, W., Perdrix, S., Porcheron, M., & Veshchezerova, M. (2021) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-021-00100-3 · Cited by 46 · see citing works
An optical tweezer array of ultracold polyatomic molecules
Vilas, N. B., Robichaud, P., Hallas, C., Li, G. K., Anderegg, L., & Doyle, J. M. (2024) · Nature · doi:10.1038/s41586-024-07199-1 · Cited by 42 · see citing works
- Authors
- Scholl, P., Schuler, M., Williams, H. J., Eberharter, A. A., Barredo, D., Schymik, K.-N., Lienhard, V., Henry, L.-P., Lang, T. C., Lahaye, T., Läuchli, A. M., & Browaeys, A.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- TU Wien (AT)
- Universität Innsbruck (AT)
- Universität Hamburg (DE)
- Corresponding author
- Scholl, Pascal
- ORCID
- Venue
- Nature
- Published
- 2021-07-07
- Volume
- 595
- Issue
- 7866
- Pages
- 233-238
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3115562073
- Cited by
- 688
- References
- 10
- Citations by year
- 2026: 80 · 2025: 152 · 2024: 133 · 2023: 139 · 2022: 144 · 2021: 38 · 2020: 2
- FWCI
- 49.66
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Cold Atom Physics and Bose-Einstein Condensates
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Keywords
- quantum simulation, antiferromagnets, Rydberg atoms, optical tweezers, transverse-field Ising model, antiferromagnetic ordering, trapped neutral atoms
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