Benchmarking a trapped-ion quantum computer with 29 algorithmic qubits

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

arXiv (Cornell University)(2023)

引用 0|浏览9
暂无评分
摘要
Quantum computers are rapidly becoming more capable, with dramatic increases in both qubit count and quality. 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 here 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 benchmarks, indicating 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, including error mitigation. We find that the system-level model correlates well with the observations in many cases, though in some cases out-of-model errors lead to higher predicted performance than 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.
更多
查看译文
关键词
algorithmic qubits,quantum,trapped-ion
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要