Biography
Natalia Ares works on experiments to advance the development of quantum technologies, with a focus on artificial intelligence for quantum device control and quantum thermodynamics. She joined the Materials Department at the University of Oxford in 2013. She was awarded a series of fellowships, including a Marie Skłodowska-Curie and a Royal Society University Research Fellowship, and was awarded a European Research Council Starting Grant in 2020. During her PhD she focused on silicon-based devices for quantum computing at CEA Grenoble, France. She completed her undergraduate studies in Physics and a Masters equivalent in the theory of quantum chaos at the University of Buenos Aires, Argentina, where she was born and raised.
Research Interests
Natalia's research is in novel approaches to advance the engineering of quantum processors. To unleash the potential of quantum technologies, the intrinsic variability between individual devices has so far been a major hindrance.
For large arrays of devices, the tuning required to account for this variability quickly becomes an intractable task, presenting a bottleneck for the realisation of large quantum circuits. Natalia's group develops novel machine learning algorithms able to control complex quantum circuits in real time.
Natalia is also interested in the thermodynamics aspects of quantum information processing. The group fabricates devices at nanometre scales and holds them at millikelvin temperatures to explore the link between information and thermodynamics in the quantum world. Their exploration of information thermodynamics in the quantum arena will inform the construction of autonomous quantum machines such as quantum engines and quantum circuits with learning capabilities.
Current Projects
- Machine learning for quantum device control.
- Thermodynamics of quantum information processing.
Research Groups
Recent Publications
Coupling a single spin to the motion of a carbon nanotube
Fedele F, Cerisola F, Bresque L, Vigneau F, Monsel J et al. (2025), Nature Communications
QuantGraph: A Receding-Horizon Quantum Graph Solver
Vaidhyanathan P, Papatheodorou A, Arvidsson-Shukur DRM, Mitchison MT, Ares N et al. (2025)
BibTeX
@misc{quantgrapharece-2025/12,
title={QuantGraph: A Receding-Horizon Quantum Graph Solver},
author={Vaidhyanathan P, Papatheodorou A, Arvidsson-Shukur DRM, Mitchison MT, Ares N et al.},
year = "2025"
}
All-rf-based coarse-tuning algorithm for quantum devices using machine learning
van Straaten B, Fedele F, Vigneau F, Hickie J, Jirovec D et al. (2025), Physical Review Applied, 24(5), 054030-054030
BibTeX
@article{allrfbasedcoars-2025/11,
title={All-rf-based coarse-tuning algorithm for quantum devices using machine learning},
author={van Straaten B, Fedele F, Vigneau F, Hickie J, Jirovec D et al.},
journal={Physical Review Applied},
volume={24},
pages={054030-054030},
publisher={American Physical Society (APS)},
year = "2025"
}
Entropic Costs of Extracting Classical Ticks from a Quantum Clock
Wadhia V, Meier F, Fedele F, Silva R, Nurgalieva N et al. (2025), Physical Review Letters, 135(20), 200407
BibTeX
@article{entropiccostsof-2025/11,
title={Entropic Costs of Extracting Classical Ticks from a Quantum Clock},
author={Wadhia V, Meier F, Fedele F, Silva R, Nurgalieva N et al.},
journal={Physical Review Letters},
volume={135},
pages={200407},
publisher={American Physical Society (APS)},
year = "2025"
}
Automatic tuning of a donor in a silicon quantum device using machine learning
Severin B, Botzem T, Fedele F, Yu X, Wilhelm B et al. (2025)
Virtual Gates Enabled by Digital Surrogate of Quantum Dot Devices
Lidiak A, Swain J, Craig DL, Hickie J, Yang Y et al. (2025)
Sources of nonlinearity in the response of a driven nano-electromechanical resonator
Sevitz S, Aggarwal K, Tabanera-Bravo J, Monsel J, Vigneau F et al. (2025)
Compromise-free scaling of qubit speed and coherence.
Carballido MJ, Svab S, Eggli RS, Patlatiuk T, Chevalier Kwon P et al. (2025), Nat Commun, 16(1), 7616
End-to-End Analysis of Charge Stability Diagrams with Transformers
Marchand R, Schorling L, Carlsson C, Schuff J, van Straaten B et al. (2025)
Rapid optimal work extraction from a quantum-dot information engine
Aggarwal K, Rolandi A, Yang Y, Hickie J, Jirovec D et al. (2025), Physical Review Research, 7(3), l032017-l032017
BibTeX
@article{rapidoptimalwor-2025/7,
title={Rapid optimal work extraction from a quantum-dot information engine},
author={Aggarwal K, Rolandi A, Yang Y, Hickie J, Jirovec D et al.},
journal={Physical Review Research},
volume={7},
pages={l032017-l032017},
publisher={American Physical Society (APS)},
year = "2025"
}
Extra cost of erasure due to quantum lifetime broadening
Dunlop J, Cerisola F, Monsel J, Sevitz S, Tabanera-Bravo J et al. (2025), Physical Review A, 112(1), l010601-l010601
BibTeX
@article{extracostoferas-2025/7,
title={Extra cost of erasure due to quantum lifetime broadening},
author={Dunlop J, Cerisola F, Monsel J, Sevitz S, Tabanera-Bravo J et al.},
journal={Physical Review A},
volume={112},
pages={l010601-l010601},
publisher={American Physical Society (APS)},
year = "2025"
}
Learning Physical Systems: Symplectification via Gauge Fixing in Dirac Structures
Papatheodorou A, Vaidhyanathan P, Ares N & Havoutis I (2025)
Quantum computing and artificial intelligence: status and perspectives
Acampora G, Ambainis A, Ares N, Banchi L, Bhardwaj P et al. (2025)
Automated All-RF Tuning for Spin Qubit Readout and Control
Carlsson C, Saez-Mollejo J, Fedele F, Calcaterra S, Chrastina D et al. (2025)
Meta-learning characteristics and dynamics of quantum systems
Schorling L, Vaidhyanathan P, Schuff J, Carballido MJ, Zumbühl D et al. (2025)