QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments.

ABSTRACT:

Background

Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such

SUBMITTER: Zwolak JP 

PROVIDER: S-EPMC6192646 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature