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

• Published in: PloS one Vol. 13; no. 10; p. e0205844
• Main Authors: Zwolak, Justyna P, Kalantre, Sandesh S, Wu, Xingyao, Ragole, Stephen, Taylor, Jacob M
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.10.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Arrays; Artificial intelligence; Artificial neural networks; Automation; Biology and Life Sciences; Cancer; Collaboration; Computation; Computational Biology - methods; Computer and Information Sciences; Computer science; Computer Security; Computer Simulation; Computers; Conductance; Cybersecurity; Data Mining; Data processing; Databases, Factual; Drug Discovery; Earth Sciences; Engineering and Technology; Information processing; Internet; Knobs; Learning algorithms; Machine Learning; Methods; Neural networks; Neural Networks (Computer); Optimization; Physical Sciences; Problem solving; Programming Languages; Quantum computers; Quantum computing; Quantum Dots; Quantum theory; Reproducibility of Results; Research and Analysis Methods; Resistance; Scholarly publishing; Semiconductors; Software packages; Training; Tuning; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0205844

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 an approach does 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 QD devices 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. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks. From 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is ≈ 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, 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.