About Quandela
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/Project
MerLin 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.
What you will learn:
- 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.
- 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.
- 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.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The emergence of dedicated software roles focused on quantum machine learning frameworks represents a structural bridge within the technology value chain, connecting theoretical physics research with standard artificial intelligence workflows. As classical software developers seek entry points into quantum processing architectures, specialized software frameworks reduce barriers by abstracting quantum circuitry into standard deep learning environments. This integration is critical for transitioning algorithms from abstract academic papers to scalable, reproducible scientific computing tools that can be validated against established enterprise baselines. Current industry focus lies on bridging classical and quantum capabilities at scale. By embedding quantum code within common machine learning pipelines, this role type facilitates initial benchmarks that demonstrate how hybrid models interact with classical high-performance computing resources. Consequently, these early-stage talent pipelines mitigate the severe workforce bottlenecks in deep tech by providing practical cross-disciplinary exposure that normalizes quantum software engineering methods.
The deployment of hybrid quantum-classical software frameworks addresses a primary bottleneck in the application layer of the quantum information science value chain. While physical quantum processing hardware advances across multiple modalities, translating these theoretical hardware capabilities into industrial utility demands a mature software ecosystem. Historically, software development in this field remained fragmented and required deep specialization in quantum mechanics. Modern ecosystem dynamics are forcing a shift toward interoperability, where quantum computing operations must function seamlessly as accelerators alongside classical high-performance computing systems and cloud-native artificial intelligence pipelines.
This integration requirement highlights a significant Technology Readiness Level gap between academic discovery and enterprise software quality. Many published quantum machine learning models are developed within isolated research environments, lack standard continuous integration protocols, and are difficult to reproduce systematically. Software functions targeted at paper reproduction and API design help establish rigorous algorithmic benchmarking and verification methods within the ecosystem.
Furthermore, these foundational software engineering positions are essential for expanding the broader industry talent pipeline. Deep-tech organizations face strict resource limitations when attempting to hire individuals possessing dual expertise in both advanced quantum mechanics and production-grade software development. By building accessible frameworks integrated with standard machine learning libraries, firms allow classical software practitioners to contribute directly to quantum software infrastructure without prior hardware-specific domain expertise.
The capability architecture for software functions in this domain bridges classical deep learning tooling layers with the abstract syntax of quantum programming libraries. Proficiency in standard scientific computing packages and tensor-based machine learning libraries is essential to construct, manage, and optimize hybrid training loops. These capabilities govern how classical data tensors are mapped into quantum states and how gradient information is effectively backpropagated through simulated quantum structures.
Beyond model implementation, structural execution relies heavily on software engineering hygiene, including continuous integration pipelines, modular API engineering, and automated validation tests. These engineering workflows ensure that added codebase features do not introduce regression errors and that software remains stable across updates to external library dependencies. This disciplined architectural approach allows teams to systematically separate actual algorithmic gaps from bugs introduced by simulation artifacts or varying hardware configurations. Ultimately, establishing these clean software practices increases internal research throughput and guarantees that software framework modifications are reliable, maintaining long-term project maintainability. - Accelerates the translation of theoretical quantum machine learning research into reproducible scientific computing software repositories
- Reduces ecosystem integration friction by abstracting quantum operations into mainstream artificial intelligence engineering pipelines
- Validates the commercial and scientific scalability of novel quantum software frameworks against established classical baselines
- Cultivates early-stage engineering pipelines by offering clear pathways for classical developers into quantum computing roles
- Mitigates software regression risks through the implementation of automated test suites and structured code reviews
- Enhances framework usability for artificial intelligence practitioners by broadening and stabilizing core application programming interfaces
- Lowers historical entry barriers for classical software engineers by leveraging standard deep learning tracking environments
- Optimizes hybrid execution speeds by refining code components at the classical-quantum software interface layer
- Standardizes empirical benchmarks for hybrid quantum-classical systems to track performance developments transparently
- Decouples software logic from underlying hardware variables to support more flexible cross-platform algorithmic development
- Promotes open-source software collaboration standards within an evolving deep-tech startup ecosystem
- Secures software framework reliability through systematic identification of simulation errors and code defectsIndustry Tags: Quantum Machine Learning, Photonic Quantum Frameworks, Hybrid Quantum-Classical AI, Deep Learning Integration, Software Engineering Hygiene, Algorithmic Benchmarking, Deep Tech Value Chain, Talent Pipeline Development
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