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
Stability of long-sustained oscillations induced by electron tunneling
Tabanera-Bravo J, Vigneau F, Monsel J, Aggarwal K, Bresque L et al. (2024), Physical Review Research, 6(1), 013291
BibTeX
@article{stabilityoflong-2024/3,
title={Stability of long-sustained oscillations induced by electron tunneling},
author={Tabanera-Bravo J, Vigneau F, Monsel J, Aggarwal K, Bresque L et al.},
journal={Physical Review Research},
volume={6},
pages={013291},
publisher={American Physical Society (APS)},
year = "2024"
}
High-fidelity spin qubit operation and algorithmic initialization above 1 K.
Huang JY, Su RY, Lim WH, Feng M, van Straaten B et al. (2024), Nature, 627(8005), 772-777
BibTeX
@article{highfidelityspi-2024/3,
title={High-fidelity spin qubit operation and algorithmic initialization above 1 K.},
author={Huang JY, Su RY, Lim WH, Feng M, van Straaten B et al.},
journal={Nature},
volume={627},
pages={772-777},
year = "2024"
}
Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning
Craig DL, Moon H, Fedele F, Lennon DT, van Straaten B et al. (2024), Physical Review X, 14(1), 011001
BibTeX
@article{bridgingthereal-2024/1,
title={Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning},
author={Craig DL, Moon H, Fedele F, Lennon DT, van Straaten B et al.},
journal={Physical Review X},
volume={14},
pages={011001},
publisher={American Physical Society (APS)},
year = "2024"
}
Probing quantum devices with radio-frequency reflectometry
Vigneau F, Fedele F, Chatterjee A, Reilly D, Kuemmeth F et al. (2023), Applied Physics Reviews, 10(2)
BibTeX
@article{probingquantumd-2023/6,
title={Probing quantum devices with radio-frequency reflectometry},
author={Vigneau F, Fedele F, Chatterjee A, Reilly D, Kuemmeth F et al.},
journal={Applied Physics Reviews},
volume={10},
year = "2023"
}
Probing quantum devices with radio-frequency reflectometry
Vigneau F, Fedele F, Chatterjee A, Reilly D, Kuemmeth F et al. (2023), APPLIED PHYSICS REVIEWS, 10(2)
BibTeX
@article{probingquantumd-2023/,
title={Probing quantum devices with radio-frequency reflectometry},
author={Vigneau F, Fedele F, Chatterjee A, Reilly D, Kuemmeth F et al.},
journal={APPLIED PHYSICS REVIEWS},
volume={10},
number={ARTN 021305},
year = "2023"
}
Ultrastrong coupling between electron tunneling and mechanical motion
Vigneau F, Monsel J, Tabanera J, Aggarwal K, Bresque L et al. (2022), Physical Review Research, 4(4)
BibTeX
@article{ultrastrongcoup-2022/10,
title={Ultrastrong coupling between electron tunneling and mechanical motion},
author={Vigneau F, Monsel J, Tabanera J, Aggarwal K, Bresque L et al.},
journal={Physical Review Research},
volume={4},
year = "2022"
}
Radio-frequency characterization of a supercurrent transistor made of a carbon nanotube
Mergenthaler M, Schupp FJ, Nersisyan A, Ares N, Baumgartner A et al. (2021), Materials for Quantum Technology, 1(3), 035003-035003
BibTeX
@article{radiofrequencyc-2021/9,
title={Radio-frequency characterization of a supercurrent transistor made of a carbon nanotube},
author={Mergenthaler M, Schupp FJ, Nersisyan A, Ares N, Baumgartner A et al.},
journal={Materials for Quantum Technology},
volume={1},
pages={035003-035003},
publisher={IOP Publishing},
year = "2021"
}
Deep reinforcement learning for efficient measurement of quantum devices
Nguyen V, Orbell S, Lennon D, Moon H, Vigneau F et al. (2021), npj Quantum Information, 7(1)
BibTeX
@article{deepreinforceme-2021/6,
title={Deep reinforcement learning for efficient measurement of quantum devices},
author={Nguyen V, Orbell S, Lennon D, Moon H, Vigneau F et al.},
journal={npj Quantum Information},
volume={7},
number={100},
publisher={Springer Nature},
year = "2021"
}
Machine learning as an enabler of qubit scalability
Ares N (2021), NATURE REVIEWS MATERIALS
BibTeX
@article{machinelearning-2021/4,
title={Machine learning as an enabler of qubit scalability},
author={Ares N},
journal={NATURE REVIEWS MATERIALS},
year = "2021"
}
Eight oxford questions: Quantum mechanics under a new light
Ares N, Pearson AN & Briggs GAD (2021), 198, 361-384
BibTeX
@misc{eightoxfordques-2021/1,
title={Eight oxford questions: Quantum mechanics under a new light},
author={Ares N, Pearson AN & Briggs GAD},
year = "2021"
}
Quantum device fine-tuning using unsupervised embedding learning
van Esbroeck NM, Lennon DT, Moon H, Nguyen V, Vigneau F et al. (2020), New Journal of Physics, 22
BibTeX
@article{quantumdevicefi-2020/9,
title={Quantum device fine-tuning using unsupervised embedding learning},
author={van Esbroeck NM, Lennon DT, Moon H, Nguyen V, Vigneau F et al.},
journal={New Journal of Physics},
volume={22},
number={095003},
publisher={IOP Publishing},
year = "2020"
}
Machine learning enables completely automatic tuning of a quantum device faster than human experts
Moon H, Lennon D, Kirkpatrick J, Esbroeck NMV, Camenzind L et al. (2020), Nature Communications , 11
BibTeX
@article{machinelearning-2020/8,
title={Machine learning enables completely automatic tuning of a quantum device faster than human experts},
author={Moon H, Lennon D, Kirkpatrick J, Esbroeck NMV, Camenzind L et al.},
journal={Nature Communications },
volume={11},
number={4161},
publisher={Springer Nature },
year = "2020"
}
Publisher Correction: Efficiently measuring a quantum device using machine learning
Lennon DT, Moon H, Camenzind LC, Zumbühl DM, Yu L et al. (2019), npj Quantum Information(1)
BibTeX
@article{publishercorrec-2019/12,
title={Publisher Correction: Efficiently measuring a quantum device using machine learning},
author={Lennon DT, Moon H, Camenzind LC, Zumbühl DM, Yu L et al.},
journal={npj Quantum Information},
publisher={Springer Science and Business Media LLC},
year = "2019"
}
A coherent nanomechanical oscillator driven by single-electron tunnelling
Wen Y, Ares N, Schupp FJ, Pei T, Briggs G et al. (2019), Nature Physics, 16(2020), 75-82
BibTeX
@article{acoherentnanome-2019/10,
title={A coherent nanomechanical oscillator driven by single-electron tunnelling},
author={Wen Y, Ares N, Schupp FJ, Pei T, Briggs G et al.},
journal={Nature Physics},
volume={16},
pages={75-82},
publisher={Nature Research},
year = "2019"
}
Efficiently measuring a quantum device using machine learning
Lennon DT, Moon H, Camenzind LC, Yu L, Zumbühl DM et al. (2019), npj Quantum Information, 5
BibTeX
@article{efficientlymeas-2019/9,
title={Efficiently measuring a quantum device using machine learning},
author={Lennon DT, Moon H, Camenzind LC, Yu L, Zumbühl DM et al.},
journal={npj Quantum Information},
volume={5},
number={79},
publisher={Springer Nature},
year = "2019"
}
