Researcher Foundations of Quantum Machine Learning (IT-GOV-INN-2024-182-GRA

at  CERN

Job Description

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The CERN Quantum Technology Initiative (CERN QTI) invites applications for a postdoctoral research position focusing on the theoretical foundations of quantum machine learning (QML) algorithms. This role aims to enhance the understanding and development of trainability, generalization, and robustness of QML algorithms towards the transition from NISQ to FTQC, where the interplay between data encoding and Quantum Singular Value Transformation-like techniques - such as block encoding and Linear Combination of Unitaries (LCU) - is crucial for achieving efficient quantum transformations that exploit the unique properties of quantum mechanics. A special emphasis on potential use cases in high energy physics (HEP) simulations as a direct application is preferred.

Key Responsibilities:

• Conduct independent research on the theoretical aspects of quantum machine learning algorithms.
• Investigate the trainability, generalization, and robustness of QML models, particularly in the context of HEP simulations.
• Explore the interplay between quantum information and quantum field theory in the development of quantum computing algorithms.
• Combine the strengths of QSVT with tailored data encoding strategies and variational components, for robust and implementable algorithms for current quantum platforms but also designed to tackle large-scale and complex problems as quantum hardware advances.
• Supervise and mentor a group of younger researchers, including PhD and Master’s students, guiding their research projects and fostering their academic growth.
• Collaborate with an interdisciplinary team of physicists, computer scientists, and engineers within CERN QTI.
• Publish research findings in leading scientific journals and present results at international conferences and workshops.