106 citations · 360 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…