Machine Learning for Rydberg-Based Quantum Simulators internship - H/F

About the team

The Quantum material department at Pasqal develop hybrid quantum classical algorithms with applications in material science and quantum many-body physics that can be run on Pasqal neutral atom quantum processing units.

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‑on experience with Pasqal’s analog QPU and emulator stack used to model such devices.
• The opportunity to learn important aspects of Pasqal’s quantum hardware.
• Mentorship from a multidisciplinary team (quantum many‑body physics, machine learning, materials science).

Required Qualifications

• Master 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).

Nice to Have

• 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 with high‑performance computing (HPC) environments.

Soft Skills

• Ability to work collaboratively in a research team.
• Strong communication skills in English.

Logistics

• Duration: 6 months
• Expected starting date: second semester of 2026
• Location: Massy (France)