Quandela stands as a global leader in quantum computing, driven by groundbreaking technology and a strategic vision for scaling quantum solutions. The company’s unique ability to offer both hardware and software solutions, along with its commitment to build energy efficient datacenters and scalability, positions it to play a key role in the next wave of innovation, and in many strategic and sovereign industrial sectors.
Join Us at the Forefront of Quantum Computing Innovation 🚀
Description of the Team/ProjectMerLin is a photonic quantum machine learning framework that brings quantum computing capabilities to AI practitioners through familiar PyTorch integrations. The internship will contribute to the development and scientific validation of MerLin by either reproducing recent quantum machine learning papers with the framework, or by improving the core codebase directly. Depending on the candidate profile and project priorities, the work may include implementing research models, building clean examples and benchmarks, extending model APIs, improving tests and documentation, or contributing to performance and usability improvements. The goal is to produce concrete, reviewable contributions that strengthen MerLin as a research and engineering tool.
Possible Missions
- Reproduce selected quantum machine learning papers using MerLin, with clear notebooks, benchmarks, and documented assumptions.
- Implement or improve quantum-classical model components in Python, PyTorch, and the MerLin codebase.
- Compare reproduced results against published baselines and identify gaps due to simulation choices, datasets, or implementation details.
- Add tests, examples, and documentation so new features are maintainable and usable by researchers and ML practitioners.
- Contribute to code reviews, issue analysis, experiment tracking, and technical reports summarizing results.
- How to connect research papers to production-quality scientific software.
- How photonic quantum circuits can be integrated into PyTorch-style machine learning workflows.
- How to design experiments, benchmarks, and tests for hybrid quantum-classical models.
- How to contribute to an open-source-style Python project with reviews and continuous integration.
Requirements
- Master 1 or Master 2 student, engineering school student, or equivalent, with a strong background in machine learning, software engineering, applied mathematics, or computer science. This position is only valid with an Internship Agreement
- Solid Python skills and practical experience with scientific computing and machine learning tools such as NumPy, PyTorch, Jupyter, and Git.
- Good understanding of machine learning workflows, including model implementation, training loops, datasets, evaluation, and reproducible experiments.
- Interest in quantum computing, photonics, or quantum machine learning is appreciated, but prior quantum expertise is not required.
- Ability to read technical papers, translate algorithms into maintainable code, and clearly document experimental assumptions and limitations.
- Careful engineering habits: tests, reproducible experiments, readable code, and comfort working through pull requests.
Benefits
- Swile Card (meal vouchers) 🍴🛒
- 50% participation in transportation costs 🚆
- Possibility of remote work 💻
- Internship Allowance between €1,200 and €1,400 per month 💰
- 1,5 days off per month, cumulative 🧳
What we also offer
A challenging and innovative work environment at the heart of quantum computing.
A diverse and collaborative company culture.
Opportunities for professional growth and skill development.
At Quandela, we believe that the strength of our team is the plurality of experiences, perspectives, and journeys. We are committed to building a respectful, inclusive, and welcoming work environment. All applications are welcome.
Skills Required
- Enrollment as Master 1 or Master 2 student, engineering school student, or equivalent (Internship Agreement required).
- Valid Internship Agreement.
- Solid Python skills.
- Practical experience with scientific computing and machine learning tools such as NumPy, PyTorch, Jupyter, and Git.
- Good understanding of machine learning workflows: model implementation, training loops, datasets, evaluation, reproducible experiments.
- Ability to read technical papers and translate algorithms into maintainable code with clear documentation of assumptions and limitations.
- Careful engineering habits: tests, reproducible experiments, readable code, comfort with pull requests and code reviews.
- Interest in quantum computing, photonics, or quantum machine learning.
What We Do
Quandela specialises in the production of photon-based quantum computing and cloud solutions, enabling organisations to achieve their quantum transformation
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