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Benchmarking a trapped-ion quantum computer with 30 qubits
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Quantum computers are rapidly becoming more capable, with dramatic increases in both qubit count \cite{kim2023evidence} and quality \cite{moses2023race}. Among different hardware approaches, trapped-ion quantum processors are a leading technology for quantum computing, with established high-fidelity operations and architectures with promising scaling. Here, we demonstrate and thoroughly benchmark the IonQ Forte system: configured as a single-chain 30-qubit trapped-ion quantum computer with all-to-all operations. We assess the performance of our quantum computer operation at the component level via direct randomized benchmarking (DRB) across all 30 choose 2 = 435 gate pairs. We then show the results of application-oriented \cite{IonQ_AQ20_2022}\cite{qedcPeerReviewed} benchmarks and show that the system passes the suite of algorithmic qubit (AQ) benchmarks up to #AQ 29. Finally, we use our component-level benchmarking to build a system-level model to predict the application benchmarking data through direct simulation. While we find that the system-level model correlates with the experiment in predicting application circuit performance, we note quantitative discrepancies indicating significant out-of-model errors, leading to higher predicted performance than what is observed. This highlights that as quantum computers move toward larger and higher-quality devices, characterization becomes more challenging, suggesting future work required to push performance further.
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“However, mobile qubit architectures, such as QCCD and optical tweezers24,25, can distribute resources more efficiently than stationary qubit architectures26–28, because qubits flow through the QPU like bits in a classical processor, with separated memory structures, data buses and logic units, each optimized for their purpose12.”
From A 98-qubit trapped-ion quantum computer with all-to-all connectivity (2026) · cites this paper as [28]
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Research works printed but not linked: 5
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- Anthony M. Polloreno, Arnaud Carignan-Dugas, Jordan Hines, Robin Blume-Kohout, Kevin Young, and Timothy Proctor. ``A theory of direct randomized benchmarking'' (2023). arXiv:2302.13853. · arXiv
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Li, J., Bertini, B., Garrahan, J. P., & Jack, R. L. (2026) · Physical review. B./Physical review. B · doi:10.1103/72kx-56lt
QTurbo: A Robust and Efficient Compiler for Analog Quantum Simulation
Zhou, J., Liu, Y., Che, S., Mitra, A., Kökcü, E., Rrapaj, E., Iancu, C., & Li, G. (2025) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3760250.3762227
Quantum error mitigation via structural encoding with classical error correction codes
Sohn, I., Lee, C., Song, W., Bae, K., & Lee, W. (2026) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-026-00474-2
Radial fast entangling gates under micromotion in trapped-ion quantum computers
Grosser, P., Galan, M. G., Savill-Brown, I., Ratcliffe, A. K., Liu, H., Vaidya, V. D., Haine, S. A., Viteri, C. R., Hope, J. J., & Mehdi, Z. (2026) · Physical Review A · doi:10.1103/zg69-17xc
Hybrid Quantum-Classical Computers: A Critical Analysis to Quantum Computational Models
Hajizade, A., Bassoli, R., & Fitzek, F. H. P. (2025) · IEEE Future Networks World Forum (FNWF) · doi:10.1109/fnwf66845.2025.11317658
Nauman, U., & Deng, M. (2026) · Quantum Information Processing · doi:10.1007/s11128-026-05077-5
Architecting Scalable Trapped Ion Quantum Computers using Surface Codes
Jones, S., & Murali, P. (2026) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3779212.3790128 · Cited by 6
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Elrod, N. J. (2026) · IEEE SoutheastCon · doi:10.1109/southeastcon63549.2026.11476365
О пространственном квантовании углового момента в физических кубитахPreprint
Спиричев, Ю. (2026) · doi:10.24108/preprints-3115099
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Chu, C.-T. B., Chen, H.-C., Hsu, T., Lo, H.-Y., Chang, M.-S., & Lin, G.-D. (2026) · New Journal of Physics · doi:10.1088/1367-2630/ae6e99
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Kim, T., & Choi, B.-S. (2026) · Quantum Information Processing · doi:10.1007/s11128-026-05201-5
Noise resilience in symmetric exchange interactions on NISQ processors
AbuGhanem, M. (2026) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-026-07757-8
Benchmarking engineered exchange interactions on NISQ hardware
AbuGhanem, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-53082-6
Realization of fermionic Laughlin state on a quantum processor
Shen, L., Lin, M., Lin, C. Y.-Y., Xiao, D., & Cao, T. (2026) · Nature Communications · doi:10.1038/s41467-026-72769-y · Cited by 3
Daka, C., & Bhattacharyya, S. (2026) · New Journal of Physics · doi:10.1088/1367-2630/ae8053 · Cited by 1
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Metadata
- Authors
- Chen, J.-S., Nielsen, E., Ebert, M., Inlek, V., Wright, K., Chaplin, V., Maksymov, A., Páez, E., Poudel, A., Maunz, P., & Gamble, J.
- Affiliations
- IonQ (United States) (US)
- IonQ, College Park, MD 20740
- ORCID
- Venue
- Quantum
- Published
- 2024-11-07
- Volume
- 8
- Pages
- 1516
- ISSN
- 2521-327X (ISSN-L)
- OpenAlex
- W4404140215
- Cited by
- 103
- References
- 33
- Citations by year
- 2026: 39 · 2025: 56 · 2024: 7
- FWCI
- 15.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Quantum (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- trapped-ion quantum computer, quantum hardware characterization
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