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Over the past decade, machine learning techniques have revolutionized howresearch is done, from designing new materials and predicting their propertiesto assisting drug discovery to advancing cybersecurity. Recently, we added tothis list by showing how a machine learning algorithm (a so-called learner)combined with an optimization routine can assist experimental efforts in therealm of tuning semiconductor quantum dot (QD) devices. Among otherapplications, semiconductor QDs are a candidate system for building quantumcomputers. The present-day tuning techniques for bringing the QD devices into adesirable configuration suitable for quantum computing that rely on heuristicsdo not scale with the increasing size of the quantum dot arrays required foreven near-term quantum computing demonstrations. Establishing a reliableprotocol for tuning that does not rely on the gross-scale heuristics developedby experimentalists is thus of great importance. To implement the machinelearning-based approach, we constructed a dataset of simulated QD devicecharacteristics, such as the conductance and the charge sensor response versusthe applied electrostatic gate voltages. Here, we describe the methodology forgenerating the dataset, as well as its validation in training convolutionalneural networks. We show that the learner's accuracy in recognizing the stateof a device is ~96.5 % in both current- and charge-sensor-based training. Wealso introduce a tool that enables other researchers to use this approach forfurther research: QFlow lite - a Python-based mini-software suite that uses thedataset to train neural networks to recognize the state of a device anddifferentiate between states in experimental data. This work gives thedefinitive reference for the new dataset that will help enable researchers touse it in their experiments or to develop new machine learning approaches andconcepts.

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