Machine Learning for Rydberg-Based Quantum Simulators internship HF

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 Pasqals analog QPU and emulator stack used to model such devices.

• The opportunity to learn important aspects of Pasqals 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)