Efficiently measuring a quantum device using machine learning

Machine learning techniques for state recognition and auto-tuning in quantum dots

(2019) Sandesh S. Kalantre et al.   npj Quantum Information

Compressed Optimization of Device Architectures for Semiconductor Quantum Devices

(2019) Adam Frees et al.   Physical Review Applied

A machine learning approach for automated fine-tuning of semiconductor spin qubits

(2019) Julian D. Teske et al.   APPLIED PHYSICS LETTERS

Loading a quantum-dot based “Qubyte” register

(2019) C. Volk et al.   npj Quantum Information

Automated tuning of inter-dot tunnel coupling in double quantum dots

(2018) C. J. van Diepen et al.   APPLIED PHYSICS LETTERS

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Interfacing spin qubits in quantum dots and donors—hot, dense, and coherent

(2017) L. M. K. Vandersypen et al.   npj Quantum Information

Computer-automated tuning of semiconductor double quantum dots into the single-electron regime

(2016) T. A. Baart et al.   APPLIED PHYSICS LETTERS

Mitigating Herding in Hierarchical Crowdsourcing Networks

(2016) Han Yu et al.   Scientific Reports

Fast Charge Sensing of a Cavity-Coupled Double Quantum Dot Using a Josephson Parametric Amplifier

(2015) J. Stehlik et al.   Physical Review Applied

Stochastic Kriging for Simulation Metamodeling

(2009) Bruce Ankenman et al.   OPERATIONS RESEARCH