Principal Investigator: Sergi Abadal
Email: abadal@ac.upc.edu
Web: https://www.n3cat.upc.edu/ ; https://sergiabadal.com/
Brief Theme Description:
Quantum computing has captured global attention for its theoretical ability to solve problems that are intractable using classical methods. However, realizing this potential and achieving a tangible quantum advantage involves overcoming a range of complex challenges that arise when scaling quantum computers.
Key architectural decisions, such as qubit array dimensioning and topology selection, are critical. Additionally, compilation tasks, including circuit mapping, partitioning, and routing, demand sophisticated, multi-dimensional approaches. These challenges often vary depending on the algorithm in question, necessitating solutions that span the entire quantum computing stack.
Within this context, supported by the WINC (ERC) and QUADRATURE (EIC) projects, we are seeking a researcher with a distinctive skill set at the intersection of AI and quantum computing. This role involves leading research in two main areas: (i) applying advanced AI techniques, such as reinforcement learning and graph neural networks, to address the aforementioned challenges, and (ii) designing architectures tailored to scaling quantum machine learning algorithms effectively.
Available Infrastructures: Access to quantum computers at CESGA and BSC.
Possible Secondments: Several possibilities in Europe (TU Delft, Equal1, EPFL) and US (Stanford, Princeton).
Keywords: Quantum Computing; Quantum Machine Learning; Computer Architecture; Quantum Circuit Compilation.
