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Cited by 103Quantum Computing Algorithms and ArchitectureQuantum Information and CryptographyQuantum Mechanics and Applications

Benchmarking a trapped-ion quantum computer with 30 qubits

Open access
Chen, Jwo-Sy, Nielsen, Erik, Ebert, Matthew, Inlek, Volkan, Wright, Kenneth, Chaplin, Vandiver, Maksymov, Andrii, Páez, Eduardo +3 more·2024·Quantum
DOI 10.22331/q-2024-11-07-1516
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Abstract

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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  • Mobile qubit architectures, such as QCCD and optical tweezers, can distribute resources more efficiently than stationary qubit architectures because qubits flow through the QPU like bits in a classical processor.

    “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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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
Publisher
Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften
Published
2024-11-07
Volume
8
Pages
1516
ISSN
2521-327X (ISSN-L)
DOI
10.22331/q-2024-11-07-1516
OpenAlex
W4404140215
Cited by
103
References
33
Citations by year
2026: 39 · 2025: 56 · 2024: 7 · 2023: 1
FWCI
15.11
Type
journal article
Language
en
Open access
Licence
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 › Computer Science › Artificial Intelligence › 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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