English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The current practice of manually tuning quantum dots (QDs) for qubitoperation is a relatively time-consuming procedure that is inherentlyimpractical for scaling up and applications. In this work, we report on the{\it in situ} implementation of a recently proposed autotuning protocol thatcombines machine learning (ML) with an optimization routine to navigate theparameter space. In particular, we show that a ML algorithm trained usingexclusively simulated data to quantitatively classify the state of a double-QDdevice can be used to replace human heuristics in the tuning of gate voltagesin real devices. We demonstrate active feedback of a functional double-dotdevice operated at millikelvin temperatures and discuss success rates as afunction of the initial conditions and the device performance. Modifications tothe training network, fitness function, and optimizer are discussed as a pathtoward further improvement in the success rate when starting both near and fardetuned from the target double-dot range.

Auto-tuning of double dot devices in situ with machine learning