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PASQAL

Machine Learning Intern, Rydberg-Based Quantum Simulators

PASQAL

Intern role in quantum software focusing on machine learning applications with Pasqal's quantum processing units. Developer training and benchmarks for hybrid quantum-classical approaches in a collaborative environment.

Posted 5/30/2026internshipMassy • 🇫🇷 FranceEntry LevelWebsite

Tech Stack

Tools & technologies
PythonPyTorchTensorflow

About the role

Key responsibilities & impact
  • 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.

Requirements

What you’ll need
  • 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.

Benefits

Comp & perks
  • 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).

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools
Neural Quantum StatesVariational Monte Carlomachine learningnumerical methodsquantum many-body systemsexact diagonalizationsupervised MLunsupervised MLkernel methodsscientific computing
Soft Skills
collaborative workstrong communication skills
Certifications
Master's degreePhD