Sandesh Kalantre

All Publications

• Torsional force microscopy of van der Waals moirés and atomic lattices. Proceedings of the National Academy of Sciences of the United States of America Pendharkar, M., Tran, S. J., Zaborski, G., Finney, J., Sharpe, A. L., Kamat, R. V., Kalantre, S. S., Hocking, M., Bittner, N. J., Watanabe, K., Taniguchi, T., Pittenger, B., Newcomb, C. J., Kastner, M. A., Mannix, A. J., Goldhaber-Gordon, D. 2024; 121 (10): e2314083121

  Abstract

  In a stack of atomically thin van der Waals layers, introducing interlayer twist creates a moiré superlattice whose period is a function of twist angle. Changes in that twist angle of even hundredths of a degree can dramatically transform the system's electronic properties. Setting a precise and uniform twist angle for a stack remains difficult; hence, determining that twist angle and mapping its spatial variation is very important. Techniques have emerged to do this by imaging the moiré, but most of these require sophisticated infrastructure, time-consuming sample preparation beyond stack synthesis, or both. In this work, we show that torsional force microscopy (TFM), a scanning probe technique sensitive to dynamic friction, can reveal surface and shallow subsurface structure of van der Waals stacks on multiple length scales: the moirés formed between bi-layers of graphene and between graphene and hexagonal boron nitride (hBN) and also the atomic crystal lattices of graphene and hBN. In TFM, torsional motion of an Atomic Force Microscope (AFM) cantilever is monitored as it is actively driven at a torsional resonance while a feedback loop maintains contact at a set force with the sample surface. TFM works at room temperature in air, with no need for an electrical bias between the tip and the sample, making it applicable to a wide array of samples. It should enable determination of precise structural information including twist angles and strain in moiré superlattices and crystallographic orientation of van der Waals flakes to support predictable moiré heterostructure fabrication.

  View details for DOI 10.1073/pnas.2314083121

  View details for PubMedID 38427599

• 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

• <i>QFlow lite</i> dataset: A machine-learning approach to the charge states in quantum dot experiments PLOS ONE Zwolak, J. P., Kalantre, S. S., Wu, X., Ragole, S., Taylor, J. M. 2018; 13 (10): e0205844

  Abstract

  Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cybersecurity. Recently, we added to this list by showing how a machine learning algorithm (a so-called learner) combined with an optimization routine can assist experimental efforts in the realm of tuning semiconductor quantum dot (QD) devices. Among other applications, semiconductor quantum dots are a candidate system for building quantum computers. In order to employ QDs, one needs to tune the devices into a desirable configuration suitable for quantum computing. While current experiments adjust the control parameters heuristically, such an approach does not scale with the increasing size of the quantum dot arrays required for even near-term quantum computing demonstrations. Establishing a reliable protocol for tuning QD devices that does not rely on the gross-scale heuristics developed by experimentalists is thus of great importance.To implement the machine learning-based approach, we constructed a dataset of simulated QD device characteristics, such as the conductance and the charge sensor response versus the applied electrostatic gate voltages. The gate voltages are the experimental 'knobs' for tuning the device into useful regimes. Here, we describe the methodology for generating the dataset, as well as its validation in training convolutional neural networks.From 200 training sets sampled randomly from the full dataset, we show that the learner's accuracy in recognizing the state of a device is ≈ 96.5% when using either current-based or charge-sensor-based training. The spread in accuracy over our 200 training sets is 0.5% and 1.8% for current- and charge-sensor-based data, respectively. In addition, we also introduce a tool that enables other researchers to use this approach for further research: QFlow lite-a Python-based mini-software suite that uses the dataset to train neural networks to recognize the state of a device and differentiate between states in experimental data. This work gives the definitive reference for the new dataset that will help enable researchers to use it in their experiments or to develop new machine learning approaches and concepts.

  View details for DOI 10.1371/journal.pone.0205844

  View details for Web of Science ID 000447638200072

  View details for PubMedID 30332463

  View details for PubMedCentralID PMC6192646