
Sandesh Kalantre
Ph.D. Student in Physics, admitted Summer 2022
Education & Certifications
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B.Tech, IIT Bombay, Engineering Physics with Honors and minor in Computer Science (2018)
All Publications
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Toward Robust Autotuning of Noisy Quantum dot Devices
PHYSICAL REVIEW APPLIED
2022; 17 (2)
View details for DOI 10.1103/PhysRevApplied.17.024069
View details for Web of Science ID 000766649700005
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Josephson detection of time-reversal symmetry broken superconductivity in SnTe nanowires
NPJ QUANTUM MATERIALS
2021; 6 (1)
View details for DOI 10.1038/s41535-021-00359-w
View details for Web of Science ID 000664665500002
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Ray-Based Framework for State Identification in Quantum Dot Devices
PRX QUANTUM
2021; 2 (2)
View details for DOI 10.1103/PRXQuantum.2.020335
View details for Web of Science ID 000674748200001
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Anomalous phase dynamics of driven graphene Josephson junctions
PHYSICAL REVIEW RESEARCH
2020; 2 (2)
View details for DOI 10.1103/PhysRevResearch.2.023093
View details for Web of Science ID 000602775900007
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Autotuning of Double-Dot Devices In Situ with Machine Learning
PHYSICAL REVIEW APPLIED
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
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Supercurrent interference in semiconductor nanowire Josephson junctions
PHYSICAL REVIEW B
2019; 100 (15)
View details for DOI 10.1103/PhysRevB.100.155431
View details for Web of Science ID 000492968000008
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Machine learning techniques for state recognition and auto-tuning in quantum dots
NPJ QUANTUM INFORMATION
2019; 5
View details for DOI 10.1038/s41534-018-0118-7
View details for Web of Science ID 000456992300001