# Bayesian ACRONYM Tuning

## TL;DR

This paper introduces Bayesian ACRONYM tuning, an algorithm that efficiently optimizes quantum gate fidelity by leveraging prior experimental data and local optimization, significantly reducing measurement requirements.

## Contribution

It presents a novel Bayesian approach to control tuning that reuses prior information, improving efficiency over traditional methods in quantum gate fidelity optimization.

## Key findings

- Achieved gate fidelity improvement from 88% to 99.95%.
- Reduced data usage to less than 1kB and fewer than 20 optimization steps.
- Demonstrated effectiveness in tuning single qubit gates.

## 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.