Natalia Ares

According to our database, Natalia Ares authored at least 10 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Quantum feedback control with a transformer neural network architecture.

Pranav Vaidhyanathan, Florian Marquardt, Mark T. Mitchison, Natalia Ares

CoRR, 2024

Fully autonomous tuning of a spin qubit.

Jonas Schuff, Miguel J. Carballido, Madeleine Kotzagiannidis, Juan Carlos Calvo, Marco Caselli, Jacob Rawling, David L. Craig, Barnaby van Straaten, Brandon Severin, Federico Fedele, Simon Svab, Pierre Chevalier Kwon, Rafael S. Eggli, Taras Patlatiuk, Nathan Korda, Dominik M. Zumbühl, Natalia Ares

CoRR, 2024

Looping in the Human: Collaborative and Explainable Bayesian Optimization.

Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Identifying Pauli spin blockade using deep learning.

Jonas Schuff, Dominic T. Lennon, Simon Geyer, David L. Craig, Federico Fedele, Florian Vigneau, Leon C. Camenzind, Andreas V. Kuhlmann, G. Andrew D. Briggs, Dominik M. Zumbühl, Dino Sejdinovic, Natalia Ares

Quantum, August, 2023

2021

Bridging the reality gap in quantum devices with physics-aware machine learning.

David L. Craig, H. Moon, Federico Fedele, Dominic T. Lennon, Barnaby van Straaten, Florian Vigneau, Leon C. Camenzind, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Dino Sejdinovic, Natalia Ares

CoRR, 2021

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning.

Brandon Severin, Dominic T. Lennon, Leon C. Camenzind, Florian Vigneau, Federico Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, Miguel J. Carballido, Simon Svab, Andreas V. Kuhlmann, F. R. Braakman, Simon Geyer, F. N. M. Froning, H. Moon, Michael A. Osborne, Dino Sejdinovic, G. Katsaros, Dominik M. Zumbühl, G. Andrew D. Briggs, Natalia Ares

CoRR, 2021

2020

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices.

V. Nguyen, S. B. Orbell, Dominic T. Lennon, Hyungil Moon, Florian Vigneau, Leon C. Camenzind, Liuqi Yu, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Dino Sejdinovic, Natalia Ares

CoRR, 2020

Quantum device fine-tuning using unsupervised embedding learning.

Nina M. van Esbroeck, Dominic T. Lennon, Hyungil Moon, V. Nguyen, Florian Vigneau, Leon C. Camenzind, Liuqi Yu, Dominik M. Zumbühl, G. Andrew D. Briggs, Dino Sejdinovic, Natalia Ares

CoRR, 2020

Machine learning enables completely automatic tuning of a quantum device faster than human experts.

Hyungil Moon, Dominic T. Lennon, James Kirkpatrick, Nina M. van Esbroeck, Leon C. Camenzind, Liuqi Yu, Florian Vigneau, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Dino Sejdinovic, Edward A. Laird, Natalia Ares

CoRR, 2020

2018

Efficiently measuring a quantum device using machine learning.

Dominic T. Lennon, Hyungil Moon, Leon C. Camenzind, Liuqi Yu, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Edward A. Laird, Natalia Ares

CoRR, 2018