# Benchmarking a trapped-ion quantum computer with 29 algorithmic qubits

**Authors:** Jwo-Sy Chen, Erik Nielsen, Matthew Ebert, Volkan Inlek, Kenneth Wright, Vandiver Chaplin, Andrii Maksymov, Eduardo Páez, Amrit Poudel, Peter Maunz, John Gamble  
**arXiv:** [2308.05071](https://arxiv.org/abs/2308.05071)  
**Date:** 2023-08-09  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.05071v1/

## Key points

- Quantum computing field is advancing rapidly with improvements in qubits
- Trapped-ion quantum processors are leading technology due to high-fidelity operations and scalability
- Researchers demonstrate and benchmark IonQ Forte system, a 30-qubit trapped-ion quantum computer with all-to-all operations
- Direct randomized benchmarking conducted at component level across all gate pairs (435 total)
- Application-oriented benchmarks performed on algorithmic qubit benchmarks up to AQ 29
- IonQ Forte system successfully passes benchmarks
- Component-level benchmarking data used to develop system-level model for predicting application benchmarking data
- System-level model generally correlates well with observed performance, but out-of-model errors occur in some cases
- Challenges associated with characterizing larger and higher quality quantum computers highlighted, further work needed to improve performance
- Study demonstrates capabilities of trapped ion quantum computer and provides insights into performance at both component and system levels

## Summary

The field of quantum computing is advancing rapidly, with significant improvements in the number and quality of qubits. Trapped-ion quantum processors are considered a leading technology for quantum computing due to their high-fidelity operations and promising scalability. In this study, the researchers demonstrate and thoroughly benchmark the IonQ Forte system, which is configured as a single-chain 30-qubit trapped-ion quantum computer with all-to-all operations. To assess the performance of their quantum computer, the researchers conducted direct randomized benchmarking (DRB) at the component level across all possible gate pairs (435 in total). They also performed application-oriented benchmarks to evaluate how well the system performs on algorithmic qubit (AQ) benchmarks up to AQ 29. The results indicate that the IonQ Forte system successfully passes these benchmarks. Furthermore, the researchers used their component-level benchmarking data to develop a system-level model that can predict application benchmarking data through direct simulation, including error mitigation techniques. The system-level model generally correlates well with observed performance; however, there are cases where out-of-model errors lead to higher predicted performance than what is actually observed. This finding highlights the challenges associated with characterizing larger and higher quality quantum computers and suggests that further work is needed to improve performance. Overall, this study demonstrates the capabilities of a trapped ion quantum computer and provides insights into its performance at both the component and system levels. The findings contribute to advancing our understanding of quantum computing technologies and highlight areas for future research to enhance performance even further.