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:10.22331/q-2024-11-07-1516

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, and John Gamble

IonQ, College Park, MD 20740

Published:	2024-11-07, volume 8, page 1516
Eprint:	arXiv:2308.05071v2
Doi:	https://doi.org/10.22331/q-2024-11-07-1516
Citation:	Quantum 8, 1516 (2024).

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Abstract

Quantum computers are rapidly becoming more capable, with dramatic increases in both qubit count [1] and quality [2]. 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 [3][4] 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.

Featured image: Schematic of IonQ Forte, showing the ion chain axis with respect to addressing Raman beams and imaging. The two acousto-optic deflector (AOD) devices directing light onto each ion are represented as the objects labeled (a); these devices are controlled by an embedded control system as shown by (b). The ions are imaged onto individual fibers from above as depicted by (c). The ions are held above the surface of an in-house designed surface ion trap (d). The output of the Raman source laser (e) is split into the two arms of a counter-propagating beam pair before being modulated by each AOD.

► BibTeX data

@article{Chen2024benchmarkingtrapped,
  doi = {10.22331/q-2024-11-07-1516},
  url = {https://doi.org/10.22331/q-2024-11-07-1516},
  title = {Benchmarking a trapped-ion quantum computer with 30 qubits},
  author = {Chen, Jwo-Sy and Nielsen, Erik and Ebert, Matthew and Inlek, Volkan and Wright, Kenneth and Chaplin, Vandiver and Maksymov, Andrii and P{\'{a}}ez, Eduardo and Poudel, Amrit and Maunz, Peter and Gamble, John},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {8},
  pages = {1516},
  month = nov,
  year = {2024}
}

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