Natalia Ares

Natalia Ares

Tutorial Fellow in Engineering
Engineering Science

I am an Associate Professor in Engineering Science with a Tutorial Fellowship at New College and I am a Royal Society University Research Fellow. I work on experiments to advance the development of quantum technologies, with a focus on artificial intelligence for quantum device control and quantum thermodynamics. I was awarded a European Research Council Starting Grant in 2020. During my PhD I focused on silicone-base devices for quantum computing at CEA Grenoble, France. I completed my undergraduate studies in Physics and a master equivalent in the theory of quantum chaos at University of Buenos Aires, Argentina, where I was born and raised. 


I tutor a range of topics, including: Components and circuits, Signal conditioning and Communications. I have tutored Wave Mechanics, Quantum Theory and Bonding and I have delivered Maths lectures. I was also a teaching assistant at the University of Buenos Aires, Argentina. 

Research Interests

My main area of research is quantum technologies. These technologies, which include quantum computing, quantum sensors and quantum simulators among others, harness the unique properties of quantum mechanics to achieve an advantage unattainable to their classical counterparts. In particular, I focus on quantum device control. I lead an effort to develop machine learning approaches to automate the real-time measurement and optimisation of complex quantum circuits. I am also developing heat and information engines at the nanoscale. 

Selected Publications

  • A.N. Pearson, Y. Guryanova, P. Erker, E.A. Laird, G.A.D. Briggs, M. Huber and N. Ares. Measuring the thermodynamic cost of timekeeping. Physical Review X 11, 021029 (2021). This article was covered at: New Scientist, Quanta Magazine and The Conversation among other magazines and scientific news outlets.
  • H. Moon, D. Lennon, J. Kirkpatrick, N. van Esbroeck, L. Camenzind, L. Yu, F. Vigneau, D. Zumbühl, G. Briggs, M. Osborne, D. Sejdinovic, E. Laird, and N. Ares. Machine learning enables completely automatic tuning of a quantum device faster than human experts. Nature Communications 11, 4161 (2020)
  • Y. Wen, N. Ares, F.J. Schupp, T. Pei, G.A.D. Briggs, and E.A. Laird. A coherent nanomechanical oscillator driven by single-electron tunneling. Nature Physics 16, 75 (2020).
  • D.T. Lennon, H. Moon, L.C. Camenzind, Liuqi Yu, D.M. Zumbühl, G.A.D. Briggs, M.A. Osborne,E.A. Laird, and N. Ares. Efficiently measuring a quantum device using machine learning. npj Quantum Information 5, 79 (2019). 
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