2026-07-23T03:25:50Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:10399 2024-03-06T10:46:45Z ddc:004 open_access

Bayesian ACRONYM Tuning Gamble, John Granade, Christopher Wiebe, Nathan Quantum Computing Randomized Benchmarking 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik John Gamble and Christopher Granade and Nathan Wiebe 2019 Is Part Of LIPIcs, Volume 135, 14th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2019) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.TQC.2019.7 urn:nbn:de:0030-drops-103995 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TQC.2019.7 eng https://creativecommons.org/licenses/by/3.0/legalcode