Responsibilities
- Develop and train Neural Quantum States (NQS + VMC), with pretraining of the NQS on datasets generated by the QPU.
- 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 wavefunctions of magnetic Hamiltonians.
- Contribute to internal tools and publications.
Qualifications
- Master’s or PhD student in quantum many-body physics.
- Proficiency in one or more programming languages such as Python or Julia.
- Demonstrated experience with machine learning methods applied to quantum many-body systems (e.g., neural quantum states, supervised and unsupervised ML, kernel methods).
- Experience with numerical methods for quantum spin systems (e.g., exact diagonalization and variational Monte Carlo).
- Familiarity with scientific computing frameworks (e.g., JAX, PyTorch, TensorFlow).
- Experience working in high-performance computing (HPC) environments.
- Ability to work collaboratively in a research team.
- Strong communication skills in English.
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Machine Learning Internship – Rydberg-Based Quantum Simulators in Massy employeur : Pasqal
Pasqal est un employeur exceptionnel qui valorise l'innovation et la collaboration au sein d'une équipe internationale dynamique. Situé à Massy, nous offrons un environnement de travail flexible avec des bureaux neufs, des opportunités de formation continue et la possibilité de participer à des conférences. Rejoindre Pasqal, c'est intégrer une start-up en pleine croissance où votre expertise en ingénierie FPGA sera essentielle pour façonner l'avenir du calcul quantique.
