Benchmarking a trapped-ion quantum computer with 30 qubits

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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Publication Details

Journal
Quantum
Published
2024-11-07
DOI
https://doi.org/10.22331/q-2024-11-07-1516
Citations
70
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
24.32
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article

Benchmarking a trapped-ion quantum computer with 30 qubits

Jwo-Sy Chen, Erik Nielsen, Eduardo Páez, Andrii Maksymov et al.
70 citations
Quantum
Quantum Computing Algorithms and Architecture
24.32
article

Benchmarking a trapped-ion quantum computer with 30 qubits

Jwo-Sy Chen, Erik Nielsen, Eduardo Páez, Andrii Maksymov, Matthew Ebert, Kenneth Wright, John King Gamble, Peter Maunz, Volkan Inlek, V. Chaplin, Amrit Poudel
article en
70 citations

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.

QuantumVol. 8
IonQ (United States) (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 0%
Quantum Computing Algorithms and Architecture
24.32
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