106 citations · 360 across the 15 of their papers we have counts for
15 papers
Fermiology and the Candidate Chiral Superconductor in Rhombohedral Tetralayer Graphene
Sandesh S. Kalantre, Ben H. Alexander, Julian May-Mann +11
Chiral superconductivity, in which the phase of the superconducting order parameter winds in momentum space, has long been sought for its close link to topological superconductivit…
Visualizing orbital magnetism in electron doped rhombohedral multilayer graphene
Owen I. Sheekey, Trevor B. Arp, Benjamin A. Foutty +24
Electron doped rhombohedral multilayer graphene at high displacement field features an exceptionally flat band minimum with near-ideal quantum geometry. Experiments in this regime…
Microwave spin resonance in epitaxial thin films of spin liquid candidate TbInO3
Sandesh S. Kalantre, Johanna Nordlander, Margaret A. Anderson +2
Minimizing the energy of a many body system tends to favor order, but classical frustration and quantum fluctuations destabilize that order. The tension between these effects can p…
QDFlow: A Python package for physics simulations of quantum dot devices
Donovan L. Buterakos, Sandesh S. Kalantre, Joshua Ziegler +2
Recent advances in machine learning (ML) have accelerated progress in calibrating and operating quantum dot (QD) devices. However, most ML approaches rely on access to large, repre…
Torsional Force Microscopy of Van der Waals Moirés and Atomic Lattices
Mihir Pendharkar, Steven J. Tran, Gregory Zaborski +13
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…
Toward Robust Autotuning of Noisy Quantum Dot Devices
Joshua Ziegler, Thomas McJunkin, E. S. Joseph +7
The current autotuning approaches for quantum dot (QD) devices, while showing some success, lack an assessment of data reliability. This leads to unexpected failures when noisy or…
Theoretical bounds on data requirements for the ray-based classification
Brian J. Weber, Sandesh S. Kalantre, Thomas McJunkin +2
The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases. For the case of identifying convex shapes of diff…
Ray-based framework for state identification in quantum dot devices
Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre +4
Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexi…
Ray-based classification framework for high-dimensional data
Justyna P. Zwolak, Sandesh S. Kalantre, Thomas McJunkin +2
While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative inform…
Anomalous Phase Dynamics of Driven Graphene Josephson Junctions
S. S. Kalantre, F. Yu, M. T. Wei +5
Josephson junctions with weak-links of exotic materials allow the elucidation of the Josephson effect in previously unexplored regimes. Further, such devices offer a direct probe o…
Auto-tuning of double dot devices in situ with machine learning
Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre +7
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 applicatio…
Josephson Detection of Time Reversal Symmetry Broken Superconductivity in SnTe Nanowires
C. J. Trimble, M. T. Wei, N. F. Q. Yuan +8
Exotic superconductors, such as high T, topological, and heavy-fermion superconductors, require phase sensitive measurements to determine the underlying pairing. Here we invest…
Supercurrent Interference in Semiconductor Nanowire Josephson Junctions
Praveen Sriram, Sandesh S Kalantre, Kaveh Gharavi +2
Semiconductor-superconductor hybrid systems provide a promising platform for hosting unpaired Majorana fermions towards the realisation of fault-tolerant topological quantum comput…
QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
Justyna P. Zwolak, Sandesh S. Kalantre, Xingyao Wu +2
Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery…
Machine Learning techniques for state recognition and auto-tuning in quantum dots
Sandesh S. Kalantre, Justyna P. Zwolak, Stephen Ragole +4
Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good…