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Quantum Computing
Mapping the Path to Useful Quantum Computing with Spin-Optical Architectures
Simulating the Fermi–Hubbard model is one of the most important problems in materials science, yet it quickly becomes intractable for classical computers. In a new study, Quandela and Walrus Computing show how a fault-tolerant spin-optical quantum computer could simulate a commercially relevant Fermi–Hubbard system in approximately two hours. The work provides one of the most…
Predicting polymer behaviour with quantum machine learning: Lessons from the PolyT project
When data is scarce and structures are complex, can quantum computing help predict how materials behave? The PolyT project explored quantum machine learning for polymer stability, bringing together Quandela, TotalEnergies, Alysophil, and MBDA to tackle a major industrial challenge. The team explored polymer thermal stability, running experiments on real quantum hardware and producing results towards…
Real-Time GPU–QPU Integration for Photonic Quantum Machine Learning using NVQLink
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…
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 both quantum-information-processing and machine learning tasks within the same experimental platform. The results highlight the growing potential of integrated photonics for quantum machine learning research…
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 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 spectral filtering, establishing a scalable building block for multi-source photonic quantum architectures.
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 and a strong bias toward gate-based systems leave critical questions unanswered, especially in photonic quantum computing. In this blog post, we introduce Merlin's design philosophy,…
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