



02679nas a2200181   4500008004100000245010000041210006900141260000900210300001300219490000700232520211600239100002402355700002602379700001602405700002002421700001902441856003702460       2018                        eng d00aQFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments0 aQFlow lite dataset A machinelearning approach to the charge stat  c2018  ae02058440 v133 a<p><span style="color: rgb(51, 51, 51); font-family: &quot;Open Sans&quot;, &quot;Lucida Grande&quot;, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serif; font-size: 14px;">Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties 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 QDs are a candidate system for building quantum computers. The present-day tuning techniques for bringing the QD devices into a desirable configuration suitable for quantum computing that rely on heuristics do not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance. To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. We show that the learner&#39;s accuracy in recognizing the state of a device is ~96.5 % in both current- and charge-sensor-based training. We also introduce a tool that enables other researchers to use this approach for further research: QFlow lite - a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts</span></p>1 aZwolak, Justyna, P.1 aKalantre, Sandesh, S.1 aWu, Xingyao1 aRagole, Stephen1 aTaylor, J., M.  uhttps://arxiv.org/abs/1809.10018