Quantum Graph Machine Learning Researcher
il y a 1 mois
About PASQAL
PASQAL is a company that designs and develops Quantum Processing Units (QPUs) and associated software tools. Our innovative technology enables us to address use cases that are currently beyond the reach of the most powerful supercomputers, including industrial application challenges and fundamental science needs. In addition to the exceptional computing power they provide, QPUs are highly energy efficient and will contribute to a significant reduction in the carbon footprint of the HPC industry.
Pasqal's Quantum Graph Machine Learning (QGML) Team
Pasqal's QGML team is a cutting-edge research and development team focused on advancing quantum computing and graph machine learning. Their work leverages neutral atom QPUs to solve complex problems in graph ML. These quantum systems naturally encode classical data like graphs by trapping atoms in a three-dimensional space, allowing us to explore novel approaches where quantum graph kernels and features can outperform classical methods.
Internship Opportunity
We are looking for an intern with a solid background in machine learning and relevant knowledge in many-body physics to reinforce our team. The intern will be working within PASQAL's quantum graph machine learning team on a project that tackles the extension of NQS to arbitrary finite systems with a particular focus on the inductive bias of such models, making them able to generalize to new unseen systems sampled from the training distribution.
Main Goals of the Project
- Implement novel approaches based on models that efficiently embed the system's symmetries, enhancing their inductive bias.
- Explore the power and the limits of machine learning in representing wave functions that lay in exponentially large Hilbert spaces.
- Help in the process of verifying the validity of the obtained results through implementations on PASQAL's QPU.
About You
We are looking for a highly motivated and talented individual with a strong background in machine learning, quantum computing, and many-body physics. The ideal candidate will have experience with programming in Python, including training machine learning models in frameworks such as PyTorch and JAX. A strong interest in graph theory, geometric deep learning, and graph neural networks is also essential. Excellent algorithm development and coding practices, as well as strong documentation and report writing skills, are required. Fluency in English is also a must.
What We Offer
- A dynamic and close-knit international team
- A key role in a growing startup
- Punctual remote work
- Offices in Massy, France
- A 6-month internship contract
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