Education & Certifications

  • B.Tech, IIT Bombay, Engineering Physics with Honors and minor in Computer Science (2018)

All Publications

  • Toward Robust Autotuning of Noisy Quantum dot Devices PHYSICAL REVIEW APPLIED Ziegler, J., McJunkin, T., Joseph, E. S., Kalantre, S. S., Harpt, B., Savage, D. E., Lagally, M. G., Eriksson, M. A., Taylor, J. M., Zwolak, J. P. 2022; 17 (2)
  • Josephson detection of time-reversal symmetry broken superconductivity in SnTe nanowires NPJ QUANTUM MATERIALS Trimble, C. J., Wei, M. T., Yuan, N. Q., Kalantre, S. S., Liu, P., Han, H., Han, M., Zhu, Y., Cha, J. J., Fu, L., Williams, J. R. 2021; 6 (1)
  • Ray-Based Framework for State Identification in Quantum Dot Devices PRX QUANTUM Zwolak, J. P., McJunkin, T., Kalantre, S. S., Neyens, S. F., MacQuarrie, E. R., Eriksson, M. A., Taylor, J. M. 2021; 2 (2)
  • Anomalous phase dynamics of driven graphene Josephson junctions PHYSICAL REVIEW RESEARCH Kalantre, S. S., Yu, F., Wei, M. T., Watanabe, K., Taniguchi, T., Hernandez-Rivera, M., Amet, F., Williams, J. R. 2020; 2 (2)
  • Autotuning of Double-Dot Devices In Situ with Machine Learning PHYSICAL REVIEW APPLIED Zwolak, J. P., McJunkin, T., Kalantre, S. S., Dodson, J. P., MacQuarrie, E. R., Savage, D. E., Lagally, M. G., Coppersmith, S. N., Eriksson, M. A., Taylor, J. M. 2020; 13 (3)


    The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the in situ implementation of a recently proposed autotuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of a double-QD device can be used to replace human heuristics in the tuning of gate voltages in real devices. We demonstrate active feedback of a functional double-dot device operated at millikelvin temperatures and discuss success rates as a function of the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range.

    View details for DOI 10.1103/PhysRevApplied.13.034075

    View details for Web of Science ID 000522555700002

    View details for PubMedID 33304939

    View details for PubMedCentralID PMC7724994

  • Supercurrent interference in semiconductor nanowire Josephson junctions PHYSICAL REVIEW B Sriram, P., Kalantre, S. S., Gharavi, K., Baugh, J., Muralidharan, B. 2019; 100 (15)
  • Machine learning techniques for state recognition and auto-tuning in quantum dots NPJ QUANTUM INFORMATION Kalantre, S. S., Zwolak, J. P., Ragole, S., Wu, X., Zimmerman, N. M., Stewart, M. D., Taylor, J. M. 2019; 5