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. Focus areas include: Post-Quantum Cryptography (PQC) – algorithm design, implementation, and migration Quantum Key Distribution (QKD) – system integration, deployment, and network operations Hybrid PQC
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, supported by adaptive scheduling and execution algorithms for distributed quantum applications, and validated in both simulated and real-world environments. Secondment periods are planned at the project
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top setup prototyping the translation of dynamic quantum algorithms such as double-bracket algorithmic cooling to applications in quantum sensing using NV centers. With whom? With Prof. Marek Gluza
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with quantum gates, algorithms, and measurement-based photonic quantum computing. • Familiarity with quantum optics simulation frameworks (QuTiP, Strawberry Fields, etc.). • Track record of mentoring
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vision research. The position is for 3 years, starting ASAP, focused on ‘Quantum Machine Learning’, with the objective of investigating hybrid classical-quantum and quantum inspired algorithms. The tasks
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open questions in the field live. Research directions include: Using AI to learn parameters and design circuits for quantum optimization algorithms (e.g., beyond QUBO formulations for QAOA) Developing AI
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meritorious if you provide transcripts with documented coursework in algorithms, theory of computing, quantum computing, and discrete mathematics. provide published work on the design or analysis of modes
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circuit d'extraction peu bruité. 2 Décodage efficace : les algorithmes LRPC classiques (décodage combinatoire, résolution de systèmes linéaires) peuvent offrir une complexité inférieure aux décodeurs
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of the following areas: - Quantum computing-specific: o Error-correcting codes o Lattice surgery o Surface codes o Resource estimation o Homology o Circuit design/algorithms - Mentorship - Open-source software
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3 – Quantum algorithms for data clustering: Clustering algorithms are of fundamental importance when dealing with large unstructured datasets and discovering new patterns and correlations therein