Episode XXV of the Warsaw Quantum Computing Group meetings!
Slides from Kareem:
Quantum machine learning (QML) is a direct promising application of the emerging quantum computing industry. Yet, many classical machine learning (ML) developers and researchers are not quite familiar with it. Some of them question the entire idea! This talk's aim will focus on bridging the gap between physicists and ML engineers and will try to quantize the ML pipeline as seen by the typical MLOps community. Besides that, it will also present the essence of the quantum phenomenon in QML either using the qubit system or the continuous variable system and how it can be used to further enhance classical ML. Finally, it will present the current status of QML and the unanswered questions.
Research Assistant at Wigner Research Centre for Physics. He is an AI Team Leader at DevisionX focusing on AutoML solutions for machine vision industries. He’s doing his master’s degree in computer engineering especially in Quantum Machine Learning (QML). He’s interested in the Continuous Variable model of quantum computing to be used in QML. Besides his work, he is an active member of Alexandria Quantum Computing Group, where he led several workshops in Qiskit since he is a Qiskit Advocate and coordinated a quantum winter school for Egyptian researchers.
This meeting is organized by the Quantum AI Foundation.