Newsroom / Press release

Quandela announces a 100,000-fold reduction in the number of components needed for fault-tolerant calculations, a major breakthrough for photonic quantum computing 

Paris, February 7, 2025 – Quandela, the European leader in photonic quantum computing, announces a major breakthrough for the sector in a scientific paper1 describing a reduction by a factor […]

Paris, February 7, 2025 – Quandela, the European leader in photonic quantum computing, announces a major breakthrough for the sector in a scientific paper1 describing a reduction by a factor of 100,000 in the number of components required for fault-tolerant calculations. Quandela’s hybrid approach, based on a technology that generates photonic qubits with unprecedented efficiency from artificial atoms (semiconductor quantum emitters), should enable the company to accelerate the scaling-up of its quantum computers. 

A photonic approach promising for error-correction and scaling challenges 

Fault-tolerant – error-free – quantum computing is crucial for the correct execution of the most impactful quantum algorithms, such as prime number factorization, linear system solving and chemical simulations. It is these algorithms that enable the most valuable use cases that “classical” computers cannot solve, notably in the energy, pharmaceutical, chemical and defense sectors. 

Among all quantum platforms, the photonic platform appears particularly promising for achieving fault tolerance, thanks to the unique ability of photons to :  

  • carry quantum information almost infinitely 
  • interconnect quantum processors via commercial optical fibers, as is the case with today’s largest network-connected computers.  

Interconnection between quantum processors is essential, in the long term, to extend the computing power of quantum computers – in a similar way to today’s networked supercomputers – whatever the platform in question. Photonic technology therefore inherently possesses the modularity that is absolutely essential for scaling up and implementing error-correction protocols. 

However, since photon loss is the main source of error in the photonic approach, the high performance of these quantum computers implies high optical transmission of the components, i.e. a high flow of photons through all the components. The big challenge is therefore to reduce the number of components (“resources”) in order to achieve the high optical transmission needed to manipulate and correct a large number of qubits, and thus achieve the high-impact calculations that outperform conventional computers. 

Quandela’s approach 100,000x less resource-intensive than other photonic competitors 

To meet this challenge, Quandela has just reported a groundbreaking scientific result that presents a method for reducing resource requirements by a factor of 100,000 compared with the photonics-only approach adopted and developed by other photonic quantum computing players in the USA and Canada.  

At the heart of this result lies the core technology of Quandela’s processors, based on semiconductor quantum emitters that generate photonic qubits with world-leading efficiency. Thanks to its hybrid approach, which uses these emitters both as photon generators and as qubits (by exploiting the spin of one of the emitter’s electrons), Quandela sets itself apart from other photonic competitors.  

Where a purely photonic approach would require around a million components to generate one logic qubit, the research team, led by Quandela’s Chief Research Officer Shane Mansfield, demonstrates that Quandela’s approach requires just 12, i.e. 100,000 (= 10^5 times ) less. This approach also greatly relaxes the optical transmission requirements of the components, and therefore the performance required for error correction. 

Significant reduction in energy consumption 

This considerable gain, which promises to reach the error-correction regime much more quickly, also makes it possible to drastically reduce the platform’s manufacturing costs and energy consumption. Quandela predicts a much lower power consumption than existing quantum platforms. In practice, while today’s large-scale high-performance computing centers consume around 20 MW, and cloud hyperscalers dedicated to AI require around 2 MW, Quandela’s largest quantum computer should keep its power consumption below 1MW. Quandela’s computers are therefore positioned as the solution for increasing the computing power needed by industry worldwide, without increasing energy consumption. 

“This breakthrough marks an important milestone for error-correcting computing with the photonic platform. By drastically reducing the resources required while maintaining the intrinsic advantages of the photonic approach, we are paving the way for the realistic industrialization of fault-tolerant quantum computing. Our unique hybrid approach demonstrates Quandela’s ability to significantly accelerate the scale-up of quantum computers, a crucial issue for the entire industry”, comments Niccolo Somaschi, co-founder and CEO of Quandela. 

Explore More

Read more

MerLin Unveiled: The First Quantum Layer for Data Scientists, Optimized for NVIDIA Accelerated Computing 

×

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

Paris, France – June 11th – 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). 
  1. 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. 
  1. 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 

Read more

French-German cooperation advances Europe’s quantum computer Lucy

×

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.

Read more

Quandela and Alysophil Renew Their Collaboration with TotalEnergies and MBDA 

×

Paris, June 4th – 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