Bayesian ACRONYM Tuning
Abstract
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 Lipshitz-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.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2019
- DOI:
- 10.48550/arXiv.1902.05940
- arXiv:
- arXiv:1902.05940
- Bibcode:
- 2019arXiv190205940G
- Keywords:
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- Quantum Physics
- E-Print:
- Source code for BACRONYM is included as well as all code needed to generate figures. Author ordering is alphabetical