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

Abstract

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'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.

arXiv comment18 pages, 6 figures, 3 tables

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Cited by in corpus (23)

• Auto-tuning of double dot devices in situ with machine learning
• Colloquium: Advances in automation of quantum dot devices control
• Autonomous tuning and charge state detection of gate defined quantum dots
• Deep Reinforcement Learning for Efficient Measurement of Quantum Devices
• Toward Robust Autotuning of Noisy Quantum Dot Devices
• Ray-based framework for state identification in quantum dot devices
• Miniaturizing neural networks for charge state autotuning in quantum dots
• Tuning arrays with rays: Physics-informed tuning of quantum dot charge states
• Automated extraction of capacitive coupling for quantum dot systems
• Data needs and challenges for quantum dot devices automation
• Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays
• Autonomous Bootstrapping of Quantum Dot Devices
• Theoretical bounds on data requirements for the ray-based classification
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• QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays
• Theory of charge stability diagrams in coupled quantum dot qubits
• Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS)
• Ray-based classification framework for high-dimensional data
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• Robust quantum dots charge autotuning using neural network uncertainty
• Learning Coulomb Diamonds in Large Quantum Dot Arrays
• QDFlow: A Python package for physics simulations of quantum dot devices