Valentin Guichard

Set up a next-generation 6-qubit photonic quantum processor prototype, testing and automating calibration processes reducing time by 50%. Created data processing pipelines to analyze processor performance in real-time (acquisition in ~ns, processing in ~ms), and proposed improvements based on quantitative metrics.

Python development of an interface (PyQT) for manipulating a quantum processor and processing GBs of real-time data with Pandas.

Python development (Scikit-Learn) of a Quantum Machine Learning algorithm for image sorting, based on the use of a 12-mode optical quantum processor (Ascella) for demonstration (white paper).

Supervised a multidisciplinary team (electronics, IT, optics) to design and test quantum protocols. Integrated a quantum processor into a cloud system.

Presented results and application prospects to stakeholders (industry, academia), and wrote technical reports to support decision-making.

Design and development of innovative solutions in quantum optics for quantum computing, integrating Machine Learning methods (Python - Scikit-Optimize - NeverGrad) to optimize single-photon emission for quantum processor development.

Definition of objectives, task prioritization, and coordination with technical and scientific teams to ensure project advancement within deadlines and quality standards.

Worked with theorists, electronics engineers, and technicians to integrate Machine Learning algorithms into quantum source manipulation, and optimized experimental processes using data-driven approaches.

Used Python (QuTiP, Scikit, Pandas, YData-Profiling) to simulate and analyze experimental data, and developed predictive models to improve quantum system performance.

Identification and evaluation of new methods for entanglement and characterization of single or entangled photons.