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Three-Qubit Randomized Benchmarking
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As quantum circuits increase in size, it is critical to establish scalable multiqubit fidelity metrics. Here we investigate, for the first time, three-qubit randomized benchmarking (RB) on a quantum device consisting of three fixed-frequency transmon qubits with pairwise microwave-activated interactions (cross-resonance). We measure a three-qubit error per Clifford of 0.106 for all-to-all gate connectivity and 0.207 for linear gate connectivity. Furthermore, by introducing mixed dimensionality simultaneous RB-simultaneous one- and two-qubit RB-we show that the three-qubit errors can be predicted from the one- and two-qubit errors. However, by introducing certain coherent errors to the gates, we can increase the three-qubit error to 0.302, an increase that is not predicted by a proportionate increase in the one- and two-qubit errors from simultaneous RB. This demonstrates the importance of multiqubit metrics, such as three-qubit RB, on evaluating overall device performance.
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“Methods such as randomized benchmarking [5], state and process tomography [6], and gateset tomography [7] are valued for measuring the performance of operations on a few qubits, yet they fail to account for errors arising from interactions with spectator qubits [8, 9].”
From Validating quantum computers using randomized model circuits (2019) · cites this paper as [8]
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Matteo, O. D., Gamble, J. K., Granade, C., Rudinger, K., & Wiebe, N. (2020) · Quantum · doi:10.22331/q-2020-11-17-364 · Cited by 14
Fast Flux Entangling Gate for Fluxonium Circuits
Chen, Y., Nesterov, K., Manucharyan, V., & Vavilov, M. (2022) · Physical Review Applied · doi:10.1103/physrevapplied.18.034027 · Cited by 12
Hardware-Efficient Microwave-Activated Tunable Coupling Between Superconducting Qubits
Mitchell, B., Naik, R., Morvan, A., Hashim, A., Kreikebaum, J. M., Marinelli, B., Lavrijsen, W., Nowrouzi, K., Santiago, D., & Siddiqi, I. A. (2021) · eScholarship (California Digital Library) · Cited by 11
Enabling High Performance Debugging for Variational Quantum Algorithms using Compressed Sensing
Hao, T., Liu, K., & Tannu, S. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589044 · Cited by 10
Vitanov, N. V. (2020) · New Journal of Physics · doi:10.1088/1367-2630/ab6a38 · Cited by 9
A quantum procedure for map generation
Wootton, J. R. (2020) · IEEE Conference on Games (CoG) · doi:10.1109/cog47356.2020.9231571 · Cited by 10
Low-temperature environments for quantum computation and quantum simulation*
Fu, H., Wang, P., Hu, Z., Li, Y., & Lin, X. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abd762 · Cited by 10
Santos, A. C. (2023) · Physical Review A · doi:10.1103/physreva.107.012602 · Cited by 9
Operational Quantum Mereology and Minimal Scrambling
Zanardi, P., Dallas, E., Andreadakis, F., & Lloyd, S. (2024) · Quantum · doi:10.22331/q-2024-07-11-1406 · Cited by 17
Kubo, K., Ho, Y., & Goto, H. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.22.024057 · Cited by 8
Scalable Evaluation of Quantum-Circuit Error Loss Using Clifford Sampling
Wang, Z., Chen, Y., Song, Z., Qin, D., Li, H., Guo, Q., Wang, H., Song, C., & Li, Y. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.080501 · Cited by 9
Measuring qubit stability in a gate-based NISQ hardware processor
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Characterizing Quantum Instruments: From Nondemolition Measurements to Quantum Error Correction
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Randomized Benchmarking beyond Groups
Chen, J., Ding, D., & Huang, C. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.030320 · Cited by 11
Linear cross-entropy benchmarking with Clifford circuits
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Hardware-efficient random circuits to classify noise in a multiqubit system
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Self-citationqprof: A gprof-Inspired Quantum Profiler
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Impact of Unreliable Devices on Stability of Quantum Computations
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A universal quantum gate set for transmon qubits with strong ZZ interactionsPreprint
Long, J., Zhao, T., Bal, M., Zhao, R., Barron, G. S., Ku, H., Howard, J., Wu, X., McRae, C. R., Deng, X., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12305 · Cited by 27
Calibrating quantum gates up to 52 qubits in a superconducting processor
Fan, D., Liu, G., Li, S., Gong, M., Wu, D., Zhang, Y., Zha, C., Chen, F., Cao, S., Ye, Y., et al. (2025) · npj Quantum Information · doi:10.1038/s41534-025-00983-5 · Cited by 9
Metadata
- Authors
- McKay, D. C., Sheldon, S., Smolin, J. A., Chow, J. M., & Gambetta, J. M.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2019-05-23
- Volume
- 122
- Issue
- 20
- Pages
- 200502-200502
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2776564547
- Cited by
- 156
- References
- 41
- Citations by year
- 2026: 12 · 2025: 17 · 2024: 13 · 2023: 22 · 2022: 23 · 2021: 37 · 2020: 20 · 2019: 12
- FWCI
- 10.61
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Army Research Office (W911NF-14-1-0124)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- transmon qubits, cross-resonance gate, coherent errors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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