Quandela’s Pilot Line Launch: A New Semiconductor Manufacturing Site

Launching at GTC Paris, MerLin democratizes quantum machine learning by integrating with classical AI tools—backed by GPU-accelerated performance 

Paris, France – June 11^th – Today, Quandela announces MerLin, a groundbreaking quantum computing framework designed for and by AI data scientists. Set to debut at NVIDIA GTC Paris, MerLin redefines quantum machine learning (QML) with a GPU-first approach, enabling researchers to simulate and benchmark algorithms beyond the limits of today’s quantum hardware. 

Quantum Meets AI: A Collaborative Future 

MerLin positions itself as the “quantum layer for data scientists” – contrasting with other quantum machine learning tools that target quantum scientists. By abstracting quantum complexity into familiar workflows (e.g., PyTorch/scikit-learn integrations), MerLin empowers AI practitioners to prototype hybrid quantum-classical models in hours, not months. Early adopters – including teams from the Perceval Quest, and researchers from Mila, NYUAD’s QML Lab and Scaleway – are collaborating with us to leverage MerLin and bridge classical and quantum workflows. 

“Quantum shouldn’t demand a PhD to use,” said Niccolo Somaschi, co-founder & CEO of Quandela. “MerLin gives data scientists a GPU-accelerated gateway to quantum advantage while ensuring their work remains compatible with real hardware today—and tomorrow. By integrating benchmarks and noise-aware validation, we’re addressing a critical gap: the lack of reproducible metrics in hybrid algorithm research.” 

“Powerful simulation tools are essential to develop better algorithms and accelerate the path to broad quantum advantage”, said Sam Stanwyck, Group Product Manager for quantum computing at NVIDIA. “MerLin solves a critical ecosystem need by opening the door for the broader research community to develop with photonic quantum circuits.” 

Key Innovations 

1. GPU-Optimized Simulators: 

• Leveraging NVIDIA CUDA-Q, MerLin delivers high-performance simulation for photonic quantum circuits, enabling tests for hardware that doesn’t yet exist (e.g., 24+ qubit systems). 

2. Benchmark-Driven Progress: 

• MerLin establishes reproducible metrics for hybrid algorithms, addressing the “benchmarking gap” in QML research—where thousands of papers lack standardized comparisons. 

• Integrated with Quandela Cloud, it enables immediate validation of GPU-optimized algorithms on real photonic hardware, studying noise impact and scalability. 

• Targets pragmatic use cases like quantum-enhanced kNN, GANs, and variational algorithms—backed by hardware-aware compilation. 

3. Photonic-First, Future-Proof: 

• Designed for today’s photonic QPUs (e.g., Perceval-based systems) but architected to adapt to next-gen hardware. 

• Features like dynamic circuit recompilation ensure code scalability across hardware generations. 

Who Uses MerLin? 

• AI/ML Practitioners: Prototype quantum layers without rewriting classical pipelines. 

• Quantum Researchers: Access photonic-specific tools (e.g., boson sampling) with GPU-accelerated simulation. 

• Enterprises: Pilot hybrid quantum-AI workflows with clear ROI benchmarks. 

“MerLin allowed us to adapt existing algorithms to a photonic-native format within a short timeframe. The platform offered useful comparative insights that contributed to our ongoing research and publication efforts”, said Dr. Louis Chen, an early user, Research Associate at the Quantum Centre of Imperial College London (Imperial QuEST) and participant in the most recent Perceval Quest.

Availability & Strategic Vision 

MerLin will be freely accessible to accelerate adoption, with enterprise tiers for advanced features. The roadmap includes: 

• Q2 2025: Stable PyTorch/scikit-learn APIs. 

• 2026+: Support for 24+ qubit photonic systems. 

Learn More: merlinquantum.ai 

WITTENSTEIN and Quandela underscore European innovative strength

Two leading technology companies from Germany and France are joining forces to help shape Europe’s future in quantum computing: attocube systems GmbH, a company of the WITTENSTEIN group and specialist in nanotechnology, and Quandela, a pioneer in photonic quantum computer technology. The companies have been working together on the development of the European quantum computer Lucy. Representatives of the owners, Management Board and senior management of the WITTENSTEIN group took advantage of a visit to Paris to meet with the Quandela team and assess the status of the joint project.

Lucy is no ordinary computer. It is based on light particles – known as photons – and belongs to a new generation of quantum computers that are opening up completely new possibilities in areas such as artificial intelligence, cyber security, and materials research. The quantum computer was commissioned by the European High Performance Computing Joint Undertaking (EuroHPC JU) following a competitive tender process won by the Quandela-attocube consortium.

The collaboration between Quandela and attocube demonstrates how European companies can work together to achieve technological excellence. While Quandela is developing the photonic quantum platform, attocube is supplying high-precision cryogenic systems—technology that generates the extremely low temperatures required for quantum processes.

The visit to France focused on technical progress and system integration. The participants discussed how quantum and classical computers can be combined even more effectively in the future—for example, for hybrid applications in AI or complex simulations.

“Lucy is more than a technical project – she is a symbol of European innovation,” said Dr. Bertram Hoffmann, CEO of WITTENSTEIN SE. Niccolo Somaschi, co-founder and CEO of Quandela, added: “Lucy stands for technological excellence and for the common goal of making Europe a world leader in quantum computing.”

Lucy is scheduled to go into operation later this year. It will be based at the French supercomputer center CEA TGCC, where it will serve as the cornerstone of a sovereign European quantum ecosystem.

Paris, June 4^th – Quandela, a leader in quantum computing, and Alysophil, a company specializing in AI-assisted continuous flow chemistry, announce that TotalEnergies and MBDA have renewed their trust by extending their initial partnership. This new commitment will allow the four partners to continue working together on the contributions of quantum computing to the study of new materials. This partnership, initiated over a year ago, represents an important milestone in the practical application of quantum technologies to industrial challenges. 

Quantum computing reinforcing AI 

The project aims to enhance the design of new molecules by combining two breakthrough technologies: quantum computing and artificial intelligence. While AI has already proven its ability to accelerate the discovery of new materials, it now faces the limitations of classical computers, particularly when it comes to accurately simulating interactions at the electronic level. The quantum approach overcomes these limitations by leveraging the fundamental properties of quantum physics, allowing direct modeling of electron behavior within molecules. 

The goal of the project is to explore new frontiers for innovative materials, such as high-performance polymers meeting the needs of industries that require materials which are more energetic, more resistant, and lighter. Machine learning algorithms, optimized to run on quantum processors, will make it possible to explore a much wider solution space than traditional approaches. 

“This partnership, initiated with two cutting-edge players in the French DeepTech ecosystem, Alysophil and Quandela, and with an industrial leader such as TotalEnergies, is a true opportunity. It allows us to explore the potential of quantum computers combined with Artificial Intelligence and to draw insights to guide our research in the field, confirming our position as a leader in science and technological progress” says Denis Gardin, Director of Innovation at MBDA. 

“The project fits perfectly into our strategy of exploring the potential benefits of Quantum Computing for our digital activities. Assessing the impact of Quantum Computing on Machine Learning-based algorithms is at the heart of our focus, particularly in the field of advanced materials” states Jean-Patrick Mascomere, Head of Scientific Computing at TotalEnergies R&D. 

Complementary expertise to accelerate the development of new materials 

The partnership is an important step in the industrialization of quantum technologies, by bringing together complementary areas of expertise. Alysophil contributes its know-how in machine learning and quantum chemistry, developing specific algorithms that will be used for quantum chemistry computations in the next phase of the project. Quandela complements this approach with its expertise in quantum machine learning, providing access to its photonic quantum processors (QPUs) and simulators, thereby significantly accelerating the prediction of chemical properties of materials. For its part, MBDA brings concrete perspectives for applying these new technologies, especially by steering the research toward the development of advanced materials. 

“Our expertise in machine learning and quantum chemistry enables us to develop tailored algorithms for this ambitious project. This collaboration paves the way for a new approach to molecular design—faster and more accurate” explains Philippe Robin, President of Alysophil. 

“Our quantum computing capabilities, combined with our expertise in quantum machine learning, allow us to explore novel approaches to material optimization. This collaboration perfectly illustrates the potential of combining quantum technologies with artificial intelligence to tackle real-world industrial challenges” says Niccolo Somaschi, co-founder and CEO of Quandela. 

This hybrid approach, combining the power of AI with the unique capabilities of quantum computers, opens the door to a new generation of molecular design tools. By simulating the behavior of electrons within molecules using photonic qubits, researchers will be able to explore complex molecular configurations much more efficiently than with conventional methods. This acceleration promises to drastically reduce the time required to develop new materials, thus contributing to faster and more sustainable innovation across industries. 

Artistic view of molecules and their electronic orbitals