Quantum simulation of 2D antiferromagnets with hundreds of Rydberg atoms

By P. Scholl, M. Schuler, H. Williams, A. Eberharter, D. Barredo, Kai-Niklas Schymik, Vincent Lienhard, L. Henry, T. C. Lang, Thierry Lahaye and 2 more. Nature, 2020

748 citations recorded by the source corpus.

Quantum simulation using synthetic systems is a promising route to solve outstanding quantum many-body problems in regimes where other approaches, including numerical ones, fail1. Many platforms are being developed towards this goal, in particular based on trapped ions2–4, superconducting circuits5–7, neutral atoms8–11 or molecules12,13. 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 states11, 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. Programmable quantum simulation of two-dimensional antiferromagnets is achieved with up to 196 neutral atoms, and the capability of the platform is demonstrated on square and triangular arrays.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/64b8306b2e5c1ab74f05ba9f2294bbed3e94b8f1

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Citations in the corpus

Ising machines as hardware solvers of combinatorial optimization problems (2022, background): “On the other hand, advances in the manipulations of many-body quantum systems using cold atoms, ions, or artiﬁcial qubits potentially allow for a way of simulating complex quantum systems without requiring the full controllability of a quantum computer 15,16,18–22 .”
Provably efficient machine learning for quantum many-body problems (2021, methodology): “( 75 , 76 ), a programmable and highly controlled platform for Ising-type quantum simulations ( 77 – 82 ).”
Quantum annealing for industry applications: introduction and review (2021, neutral): “Since then, other technologies have shown some promise in implementing QCs such as trapped ions [46, 151], cold atoms [94, 164, 188], photon-based quantum computers [185], Rydberg atoms [55, 164], and diamond cavities [51].”
Practical quantum advantage in quantum simulation (2022, background): “30,31 ), to systems with topological properties 32,33 , and dynamics in spin systems with both short-range 34,35 and long-range 36–38 interactions.”
Prediction of Toric Code Topological Order from Rydberg Blockade (2020, background): “[56] Pascal Scholl, Michael Schuler, Hannah J.”
Logical quantum processor based on reconfigurable atom arrays (2023, background): “This process realizes a high-fidelity, fault-tolerant transversal CNOT in a single parallel step.”