J.
P.
Zwolak
Ziegler, J. ., McJunkin, T. ., Joseph, E. S., Kalantre, S. S., Harpt, B. ., Savage, D. E., … Zwolak, J. P. (2022). Toward Robust Autotuning of Noisy Quantum dot Devices. Physical Review Applied, 17. http://doi.org/10.1103/PhysRevApplied.17.024069 (Original work published February 2022)
Zwolak, J. P., Kalantre, S. S., . Y. Wu, X. ., Ragole, S. ., & Taylor, J. M. (2018). QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments. Plos One, 13. http://doi.org/10.1371/journal.pone.0205844 (Original work published October 2018)
Zwolak, J. P., McJunkin, T. ., Kalantre, S. S., Dodson, J. P., MacQuarrie, E. R., Savage, D. E., … Taylor, J. M. (2020). Autotuning of Double-Dot Devices In Situ with Machine Learning. Physical Review Applied, 13. http://doi.org/10.1103/PhysRevApplied.13.034075 (Original work published April 2020)
Zwolak, J. P., McJunkin, T. ., Kalantre, S. S., Neyens, S. F., MacQuarrie, E. R., Eriksson, M. A., & Taylor, J. M. (2021). Ray-Based Framework for State Identification in Quantum Dot Devices. Prx Quantum, 2. http://doi.org/10.1103/PRXQuantum.2.020335 (Original work published June 2021)
Kalantre, S. S., Zwolak, J. P., Ragole, S. ., . Y. Wu, X. ., Zimmerman, N. M., Stewart, M. D., & Taylor, J. M. (2019). Machine learning techniques for state recognition and auto-tuning in quantum dots. Npj Quantum Information, 5. http://doi.org/10.1038/s41534-018-0118-7 (Original work published January 2019)