S. Kalantre

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)

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)

Trimble, C. J., Wei, M. T., Yuan, N. F. Q., Kalantre, S. S., Liu, P. ., Han, H. J., … Williams, J. R. (2021). Josephson detection of time-reversal symmetry broken superconductivity in SnTe nanowires. Npj Quantum Materials, 6. http://doi.org/10.1038/s41535-021-00359-w

Sriram, P. ., Kalantre, S. S., Gharavi, K. ., Baugh, J. ., & Muralidharan, B. . (2019). Supercurrent interference in semiconductor nanowire Josephson junctions. Physical Review B, 100. http://doi.org/10.1103/PhysRevB.100.155431

Kalantre, S. S., Yu, F. ., Wei, M. T., Watanabe, K. ., Taniguchi, T. ., Hernandez-Rivera, M. ., … Williams, J. R. (2020). Anomalous phase dynamics of driven graphene Josephson junctions. Physical Review Research, 2. http://doi.org/10.1103/PhysRevResearch.2.023093

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)