Current quantum computing infrastructures were designed around a cloud execution model in which the Quantum Processing Unit (QPU) is treated as a remote resource. In this model, a machine learning workload running on a GPU must hand data back to a classical host CPU, which coordinates the application workflow and quantum job submission. The system…
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 […]
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 Officer) and our Device R&D teams, we demonstrate high-fidelity interference between photons emitted from independent quantum dot–cavity sources. We achieve 88 ± 1% indistinguishability without…
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 […]
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 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 […]
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