Publiée 2 juillet 2026
Machine Learning for Rydberg-Based Quantum Simulators internship - W/M
Pasqal
Palaiseau, Île-de-France 91120, France
CDI
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
What we offer
Required Qualifications
Hard Skills
Nice to Have
Soft Skills
Logistics
Département Software Poste Quantum Materials Localisations Massy, France Statut à distance Hybride Type de contrat Stage Département Algorithms & Use Cases Équipe Quantum Software Ancienneté Intern
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)
Département Software Poste Quantum Materials Localisations Massy, France Statut à distance Hybride Type de contrat Stage Département Algorithms & Use Cases Équipe Quantum Software Ancienneté Intern
