Quantum (Nov 2024)

Benchmarking a trapped-ion quantum computer with 30 qubits

  • Jwo-Sy Chen,
  • Erik Nielsen,
  • Matthew Ebert,
  • Volkan Inlek,
  • Kenneth Wright,
  • Vandiver Chaplin,
  • Andrii Maksymov,
  • Eduardo Páez,
  • Amrit Poudel,
  • Peter Maunz,
  • John Gamble

DOI
https://doi.org/10.22331/q-2024-11-07-1516
Journal volume & issue
Vol. 8
p. 1516

Abstract

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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.