Benchmarking a trapped-ion quantum computer with 29 algorithmic qubits
- URL: http://arxiv.org/abs/2308.05071v1
- Date: Wed, 9 Aug 2023 17:02:55 GMT
- Title: 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 P\'aez, Amrit Poudel,
Peter Maunz, John Gamble
- Abstract summary: We demonstrate and thoroughly benchmark the IonQ Forte system.
We show that the system passes the suite of algorithmic qubit (AQ) benchmarks up to #AQ 29.
This highlights that as quantum computers move toward larger and higher-quality devices, characterization becomes more challenging.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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