KTH is looking for a doctoral student in Machine Learning for Reliable Quantum Computing. This doctoral project, funded within WASP, focuses on developing machine learning methods for reliable quantum computing. The project treats quantum noise and quantum error mitigation as problems in physics-informed, structured machine learning, with particular emphasis on learning error processes from limited, noisy measurement data. The overall goal is to develop scalable and robust models that improve the reliability of quantum algorithms in cryptography, optimization, and quantum chemistry. The work will be carried out at KTH’s Department of Computational Science and Technology in an international research environment and in close interaction with the WASP Graduate School. We are looking for candidates with a strong background in computer science, quantum computing, machine learning, engineering physics, or a related field.
Supervision: Stefano Markidis is proposed to supervise the doctoral student. Decisions are made on admission.
