Machine Learning for Rydberg-Based Quantum Simulators internship - W/M

Job description

About the team The Quantum material department at Pasqal develop hybrid quantum classical algorithms with applications in material science and quantum many-body physics and that can be run on Pasqal neutral atom quantum processing units. We are offering an internship position to work on a project involving the application of machine learning (ML) techniques to datasets generated by Rydberg quantum simulators. The goal is to develop hybrid quantum-classical approaches that combine classical ML methods with data from quantum simulators to help overcome current challenges in quantum simulations. Examples of concrete applications include finding ground states of many-body quantum Hamiltonians describing realistic magnetic materials or simulating their quantum dynamics. Mission: Develop and train Neural Quantum States (NQS + VMC), with pretraining of the NQS on QPU-generated datasets. Benchmark this approach against established numerical methods (e.g., exact diagonalization, standard VMC, tensor networks) and against raw QPU data. Apply NQS to represent observables and many-body wave functions of magnetic Hamiltonians. Contribute to internal tools and publications. What we offer: Hands-...