Natalia Ares

According to our database, Natalia Ares authored at least 16 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

End-to-End Analysis of Charge Stability Diagrams with Transformers.

Rahul Marchand, Lucas Schorling, Cornelius Carlsson, Jonas Schuff, Barnaby van Straaten, Taylor L. Patti, Federico Fedele, Joshua Ziegler, Parth Girdhar, Pranav Vaidhyanathan, Natalia Ares

CoRR, August, 2025

Learning Physical Systems: Symplectification via Gauge Fixing in Dirac Structures.

Aristotelis Papatheodorou, Pranav Vaidhyanathan, Natalia Ares, Ioannis Havoutis

CoRR, June, 2025

Quantum computing and artificial intelligence: status and perspectives.

Giovanni Acampora, Andris Ambainis, Natalia Ares, Leonardo Banchi, Pallavi Bhardwaj, Daniele Binosi, G. Andrew D. Briggs, Tommaso Calarco, Vedran Dunjko, Jens Eisert, Olivier Ezratty, Paul Erker, Federico Fedele, Elies Gil-Fuster, Martin Gärttner, Mats Granath, Markus Heyl, Iordanis Kerenidis, Matthias Klusch, Anton Frisk Kockum, Richard Kueng, Mario Krenn, Jörg Lässig, Antonio Macaluso, Sabrina Maniscalco, Florian Marquardt, Kristel Michielsen, Gorka Muñoz-Gil, Daniel Müssig, Hendrik Poulsen Nautrup, Evert P. L. van Nieuwenburg, Roman Orus, Jörg Schmiedmayer, Markus Schmitt, Philipp Slusallek, Filippo Vicentini, Christof Weitenberg, Frank K. Wilhelm

CoRR, May, 2025

A Physics-Inspired Optimizer: Velocity Regularized Adam.

Pranav Vaidhyanathan, Lucas Schorling, Natalia Ares, Michael A. Osborne

CoRR, May, 2025

Meta-learning characteristics and dynamics of quantum systems.

Lucas Schorling, Pranav Vaidhyanathan, Jonas Schuff, Miguel J. Carballido, Dominik M. Zumbühl, Gerard Milburn, Florian Marquardt, Jakob N. Foerster, Michael A. Osborne, Natalia Ares

CoRR, March, 2025

MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence.

Pranav Vaidhyanathan, Aristotelis Papatheodorou, Mark T. Mitchison, Natalia Ares, Ioannis Havoutis

CoRR, February, 2025

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, Daniel Chrastina, Giovanni 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