Qilimanjaro Quantum Tech
Pol Forn-Díaz has a diverse work experience spanning various roles and organizations within the field of quantum computing and quantum information. In 2019, they assumed the role of Group Leader of the Quantum Computing Technology Group at IFAE. Prior to that, they co-founded Qilimanjaro in 2017, a deep-tech startup focused on developing a full-stack analog quantum computer platform. Pol also served as a Scientific Advisor at Entanglement Partners sl, contributing their expertise in experimental quantum information. In addition to that, Pol worked as a Researcher at the Barcelona Supercomputing Center and as a Teaching Associate at Universitat Politècnica de Catalunya. Pol gained valuable research experience as a Postdoctoral Researcher at the Institute for Quantum Computing at the University of Waterloo, focusing on implementing quantum optics techniques to superconducting quantum circuits. Furthermore, they held a Postdoctoral position at the California Institute of Technology, where they conducted research on laser cooling and trapping single atoms for quantum information applications. Pol also spent some time as a Visiting Scholar at the Massachusetts Institute of Technology. Pol completed their PhD at TU Delft, focusing on quantum information and quantum computation using superconducting flux qubits. Overall, their work experience demonstrates a strong understanding and expertise in the field of quantum computing and quantum information.
Pol Forn-Díaz earned a Llicenciat degree in Physics from Universitat de Barcelona in 2005. Pol then pursued a doctoral degree at Delft University of Technology, specializing in Condensed Matter and Materials Physics. Pol completed their PhD from 2005 to 2010. Prior to this, in 2004 to 2005, they studied Physics at Uppsala University.
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Qilimanjaro Quantum Tech
Qilimanjaro’s integrated hardware & software team focuses on coherent quantum annealing high-quality qubit architectures to deliver scalable app-specific fully quantum processors and algorithmic services in a short timeframe.Qilimanjaro focuses on time-to-market by designing coherent quantum annealers, which do not require quantum errorcorrection and can be therefore faster-to-market. Annealers are known to optimally address certain problem categories, specifically optimization problems and Quantum Machine Learning.These categories are central to many real-world use cases across industries (logistics, traffic management, financial risk assessments, etc.), and will garner a clear demand.Specifically, Qilimanjaro’s architecture is designed on the basis of coherent qubits, whereby longer coherent times allow complex calculations to be performed in the real quantum regime, leveraging true quantum phenomena and therefore its computing capabilities.Existing quantum annealing industry players are focusing on incoherent qubits and, so far, do not show indications of exceeding conventional computing capabilities.