معرفی
Natalia Ares is an Associate Professor in the Materials Department at the University of Oxford and a Tutorial Fellow at New College. She holds a Royal Society University Research Fellowship and joined Oxford in 2013. Her research focuses on advancing quantum technologies through machine learning for device control and quantum thermodynamics.
Education:
- PhD in quantum computing (silicon-based devices) from CEA Grenoble, France
- Undergraduate and Master's in Physics with specialization in quantum chaos theory from University of Buenos Aires, Argentina
Research Focus: Dr. Ares develops machine learning algorithms to control quantum circuits in real-time, addressing device variability challenges. Her group explores quantum thermodynamics at nanoscales, fabricating devices cooled to millikelvin temperatures to study information-thermodynamics relationships, aiming to build autonomous quantum machines like quantum engines and learning circuits. Current projects include machine learning for quantum control and thermodynamics of quantum information processing.
Publication Trends (2024-2025): Recent works demonstrate strong integration of machine learning with quantum physics, focusing on quantum-dot systems, thermodynamic costs of quantum operations, symplectic learning frameworks, and high-fidelity qubit control. Publications consistently bridge theoretical quantum physics with practical engineering solutions.
Awards and Recognition:
- Marie Skłodowska-Curie Fellowship
- Royal Society University Research Fellowship
- European Research Council Starting Grant (2020)
Research Leadership: Leads the Machine Learning research group developing algorithms for quantum control. Manages experimental labs fabricating nanoscale quantum devices operating at cryogenic temperatures. Secured significant funding including ERC Starting Grant for quantum thermodynamics research.
