QFlow-lite

This document is intended as a User Guide for anyone interested in training a machine learning (ML) model using the quantum dot dataset available on data.nist.gov. For more details about the project, pease refer to the Project description document.

I. Relevant references

Details about the original dataset, as used in QFlow-lite, can be found in J.P. Zwolak et al., QFlow lite dataset: A machinelearning approach to the charge states in quantum dot experiments. PLoS ONE 13(10): e0205844 (2018).

The dataset incorporating physical noise is discussed in J. Ziegler et al., Toward Robust Autotuning of Noisy Quantum Dot Devices. arXiv:2108.00043 (2021).

II. Full list of references using the QFlow dataset

1. S.S. Kalantre, J.P. Zwolak, S. Ragole, X. Wu, N.M. Zimmerman, M.D. Stewart, and J.M. Taylor. Machine learning techniques for state recognition and auto-tuning in quantum dots. npj Quantum Inf. 5, 6 (2019).
2. J.P. Zwolak, S.S. Kalantre, X. Wu, S. Ragole, and J.M. Taylor. QFlow lite dataset: A machinelearning approach to the charge states in quantum dot experiments. PLoS ONE 13(10): e0205844 (2018).
3. J.P. Zwolak, T. McJunkin, S.S. Kalantre, J.P. Dodson, E.R. MacQuarrie, D.E. Savage, M.G. Lagally, S.N. Coppersmith, M.A. Eriksson, and J.M. Taylor. Autotuning of Double-Dot Devices In Situ with Machine Learning. Phys. Rev. Applied 13(3), 034075 (2020).
4. J.P. Zwolak, S.S. Kalantre, T. McJunkin, B.J. Weber, and J.M. Taylor. Ray-based classification framework for high-dimensional data. arXiv:2010.00500 (2020).
5. J.P. Zwolak, T. McJunkin, S.S. Kalantre, S.F. Neyens, E. R. MacQuarrie, M.A. Eriksson, and J.M. Taylor. Ray-based framework for state identification in quantum dot devices. PRX Quantum 2(2), 020335 (2021).
6. J. Ziegler, T. McJunkin, E.S. Joseph, S.S. Kalantre, B. Harpt, D.E. Savage, M.G. Lagally, M.A. Eriksson, J.M. Taylor, and J.P. Zwolak. Toward Robust Autotuning of Noisy Quantum Dot Devices. arXiv:2108.00043 (2021).