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

Descripción del puesto:

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-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
Hard Skills
* 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)