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.

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

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).

Machine Learning Intern, Rydberg-Based Quantum Simulators

Key skills