Sandesh Kalantre

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)

  View details for DOI 10.1103/PhysRevApplied.17.024069

  View details for Web of Science ID 000766649700005

• 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)

  View details for DOI 10.1038/s41535-021-00359-w

  View details for Web of Science ID 000664665500002

• 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)

  View details for DOI 10.1103/PRXQuantum.2.020335

  View details for Web of Science ID 000674748200001

• 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)

  View details for DOI 10.1103/PhysRevResearch.2.023093

  View details for Web of Science ID 000602775900007

• 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)

  Abstract

  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)

  View details for DOI 10.1103/PhysRevB.100.155431

  View details for Web of Science ID 000492968000008

• 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

  View details for DOI 10.1038/s41534-018-0118-7

  View details for Web of Science ID 000456992300001