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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 then submits the quantum job, waits for scheduling, executes it on the QPU, retrieves and deserializes the results, and finally transfers tensors back to GPU memory before inference can continue. While acceptable for experimentation and offline workloads, this architecture introduces latencies measured in seconds and prevents tight coupling between AI models and quantum processors.
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,…
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]. By combining variational methods with remeshing-inspired warm starts, we show how to scale beyond typical optimization limits. Introduction Imagine crack forming in a dam. At…
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 building a long-term partnership with South Korea as a key hub for quantum technologies, with a focus on accelerating research, talent development, and the industrialization…
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