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Measuring the capabilities of quantum computers


Quantum computers can now run interesting programs, but each processor’s capability—the set of programs that it can run successfully—is limited by hardware errors. These errors can be complicated, making it difficult to accurately predict a processor’s capability. Benchmarks can be used to measure capability directly, but current benchmarks have limited flexibility and scale poorly to many-qubit processors. We show how to construct scalable, efficiently verifiable benchmarks based on any program by using a technique that we call circuit mirroring. With it, we construct two flexible, scalable volumetric benchmarks based on randomized and periodically ordered programs. We use these benchmarks to map out the capabilities of twelve publicly available processors, and to measure the impact of program structure on each one. We find that standard error metrics are poor predictors of whether a program will run successfully on today’s hardware, and that current processors vary widely in their sensitivity to program structure.

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Fig. 1: A scalable method for benchmarking a quantum computer’s capability.
Fig. 2: Randomized benchmarks do not predict structured circuit performance.
Fig. 3: Empirical capability regions.

Data availability

All data used in this work are available at

Code availability

The data analysis code used to produce all the results presented in this work is available at The circuit sampling code is available in pyGSTi39,40,41.


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This work was supported by the United States Department of Energy, Office of Science, Office of Advanced Scientific Computing Research through the Quantum Testbed programme and the Accelerated Research in Quantum Computing (ARQC) programme, and the Laboratory-Directed Research and Development programme at Sandia National Laboratories. Sandia National Laboratories is a multi-programme laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the United States Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. All statements of fact, opinion or conclusions contained herein are those of the authors and should not be construed as representing the official views or policies of the United States Department of Energy or the United States Government, or the views of IBM or Rigetti Computing. We thank both the IBM Q and Rigetti Computing teams for extensive technical support, in particular A. Brown, J. Chow, J. Gambetta, S. Hassinger, A. Javadi, F. J. Martin Fernandez, P. Karalekas, R. Karle, D. McClure, D. McKay, P. Nation, N. Ochem, C. Osborn, E. Peterson, D. Moreda Rodriguez, M. Skilbeck, M. Tod and C. Wood.

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Authors and Affiliations



T.P., K.Y. and R.B.-K. developed the methods, designed the experiments, analysed the data and wrote the manuscript. T.P., K.R., K.Y. and E.N. wrote the circuit sampling, data collection and data analysis code. K.R. ran the experiments.

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Correspondence to Timothy Proctor.

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Peer review information Nature Physics thanks the anonymous reviewers for their contribution to the peer review of this work

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Supplementary Information

Supplementary Notes 1–9 and Figs. 1–5.

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Proctor, T., Rudinger, K., Young, K. et al. Measuring the capabilities of quantum computers. Nat. Phys. 18, 75–79 (2022).

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