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Learn the latest about Photonic
Quantum Computing
Advancing Photonic Quantum Machine Learning with Silicon Photonics
Machine learning systems are becoming increasingly powerful, but also increasingly computationally demanding. In our recent work with our partners from the QUONDENSATE consortium, we explore how Quandela’s QPUs can perform […]
Indistinguishable Photons from Independent Quantum Dot Sources: Enabling Spin-Optical Quantum Computing
Scalable photonic quantum computing requires multiple independent single-photon sources that behave as a unified quantum resource. In collaboration between the C2N (under the expertise of Pascale Senellart, Quandela’s Chief Scientific […]
Merlin: Bridging the reproducibility gap in Quantum Machine Learning
Merlin is a community framework for systematic, reproducible quantum machine learning research — built to close the gap between QML claims and working code. As QML grows rapidly, reproducibility failures […]
Cracking fracture mechanics on a quantum processor… and it shouldn’t start from scratch
Can quantum computers help predict how materials break? We introduce a quantum algorithm for fracture mechanics that encodes elastic systems efficiently and retrieves local crack information with few measurements [1]. […]
Quandela Expands in South Korea: From Research to Real-World Quantum Impact
Quandela took part in the French President’s state visit to South Korea, marking 140 years of diplomatic relations between the two countries. On this occasion, Quandela reaffirmed its commitment to […]
Turning Fundamental Physics into Quantum Technology: The Journey of Pascale Senellart
As quantum computing progresses beyond the lab, photonic approaches are moving from demonstrations toward deployable systems. Along with her Quandela co-founders Niccolò Somaschi and Valérian Giesz, Pascale Senellart, CNRS research […]
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