Machine Learning For Physicists (ePub)

Sadegh Raeisi has a background in Quantum Computing and Quantum Information Science. He completed his MSc at the University of Calgary and his Ph.D. at the Institute for Quantum Computing at the University of Waterloo, as well as a Postdoc at the Max Planck Institute for the Science of Light in Erlangen. He then moved back to his home country and has held a faculty position since 2017. With about 18 years of research experience within the field of Quantum Computing, Sadegh is probably most recognized for his pioneering works on Macroscopic Quantumness and algorithmic cooling, including finding the cooling limit of Heat-bath Algorithmic Cooling (HBAC) techniques which was an open problem for a decade, and for inventing the Blind HBAC technique, which is the optimal and practical HBAC technique.

Sedighe Raeisi has a background in high-energy physics, nonlinear dynamics and chaotic systems. She holds a Ph.D. from Ferdowsi University of Mashhad where she also worked for 2 years as a lecturer after graduation. Her areas of expertise include machine learning and deep learning with special focus on Natural Language Processing (NLP), Machine Vision, Graph Neural networks and time series forecasting. She is currently working as a Data scientist in the Research and Development division of Iran's largest telecommunications company.

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