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Bayesian ACRONYM Tuning

Authors John Gamble, Christopher Granade, Nathan Wiebe

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Author Details

John Gamble
  • Quantum Architectures and Computing Group, Microsoft Research, Redmond WA, USA
Christopher Granade
  • Quantum Architectures and Computing Group, Microsoft Research, Redmond WA, USA
Nathan Wiebe
  • Quantum Architectures and Computing Group, Microsoft Research, Redmond WA, USA


This project was prepared using a reproducible workflow [Granade, 2017].

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John Gamble, Christopher Granade, and Nathan Wiebe. Bayesian ACRONYM Tuning. In 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 135, pp. 7:1-7:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


We provide an algorithm that uses Bayesian randomized benchmarking in concert with a local optimizer, such as SPSA, to find a set of controls that optimizes that average gate fidelity. We call this method Bayesian ACRONYM tuning as a reference to the analogous ACRONYM tuning algorithm. Bayesian ACRONYM distinguishes itself in its ability to retain prior information from experiments that use nearby control parameters; whereas traditional ACRONYM tuning does not use such information and can require many more measurements as a result. We prove that such information reuse is possible under the relatively weak assumption that the true model parameters are Lipschitz-continuous functions of the control parameters. We also perform numerical experiments that demonstrate that over-rotation errors in single qubit gates can be automatically tuned from 88% to 99.95% average gate fidelity using less than 1kB of data and fewer than 20 steps of the optimizer.

Subject Classification

ACM Subject Classification
  • Hardware → Quantum computation
  • Quantum Computing
  • Randomized Benchmarking


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