Sandesh Kalantre | QuICS

Lanczos Graduate Student (2018-2020)

skalantr@umd.edu

2231 Atlantic Building

Sandesh is a doctoral student in physics. He is interested in applying modern computational techniques like classical machine learning to efficiently bootstrap quantum experiments. He also works broadly on topological quantum materials and in particular on Josephson junction experiments. Sandesh held a QuICS Lanczos Graduate Fellowship 2018 to 2020. His advisor is James Williams in JQI.

Publications

2022

J. Ziegler, McJunkin, T., Joseph, E. S., Kalantre, S. S., Harpt, B., Savage, D. E., Lagally, M. G., Eriksson, M. A., Taylor, J. M., and Zwolak, J. P., “Toward Robust Autotuning of Noisy Quantum dot Devices”, Physical Review Applied, vol. 17, 2022.

B. J. Weber, Kalantre, S. S., McJunkin, T., Taylor, J. M., and Zwolak, J. P., “Theoretical bounds on data requirements for the ray-based classification”, SN Comput. Sci., vol. 3, no. 57, 2022.

2021

J. P. Zwolak, McJunkin, T., Kalantre, S. S., Neyens, S. F., MacQuarrie, E. R., Eriksson, M. A., and Taylor, J. M., “Ray-based framework for state identification in quantum dot devices”, PRX Quantum, vol. 2, no. 020335, 2021.

2020

J. P. Zwolak, McJunkin, T., Kalantre, S. S., Dodson, J. P., MacQuarrie, E. R., Savage, D. E., Lagally, M. G., Coppersmith, S. N., Eriksson, M. A., and Taylor, J. M., “Auto-tuning of double dot devices in situ with machine learning”, Phys. Rev. Applied , vol. 13, no. 034075 , 2020.

J. P. Zwolak, Kalantre, S. S., McJunkin, T., Weber, B. J., and Taylor, J. M., “Ray-based classification framework for high-dimensional data”, Proceedings of the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020, Vancouver, Canada, 2020.

2018

J. P. Zwolak, Kalantre, S. S., Wu, X., Ragole, S., and Taylor, J. M., “QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments”, PLOS ONE, vol. 13, no. 10, p. e0205844, 2018.

2017

S. S. Kalantre, Zwolak, J. P., Ragole, S., Wu, X., Zimmerman, N. M., Stewart, M. D., and Taylor, J. M., “Machine Learning techniques for state recognition and auto-tuning in quantum dots”, 2017.