Quantum K-Means

Machine learning can make impressive predictions while raising equally important questions. We interview Jonas Landman, a Postdoc candidate at the University of Edinburg. Jonas discusses his study around quantum learning where he attempted to recreate the conventional k-means clustering algorithm and spectral clustering algorithm using quantum computing.

Guest

Jonas Landman: I am an INRIA permanent researcher in Quantum Computing and Machine Learning in the QAT team at the Computer Science dept. of École Normale Supérieure Ulm. My current focus is quantum algorithms and/or classical neural networks for applications in quantum physics, simulation and chemsitry. I was a Postdoc at the University of Edinburgh (Scotland, UK), leading the QML group under the direction of Pr. Elham Kahesfi. With strong ties with the Quantum Software Lab, and the LIP6 lab at Sorbonne Université (Paris). I was also a staff scientist at QC Ware, a Palo Alto (US) based startup in quantum algorithms and computational chemistry. I did my PhD (2018-2021) under the supervision of Pr. Iordanis Kerenidis at the IRIF lab of Université Paris Cité. Before that, I studied at UC Berkeley (US) and École Polytechnique (France).