Master quantum computing fundamentals from quantum mechanics principles to modern information processing models for computation and machine learning
Master quantum computing fundamentals from quantum mechanics principles to modern information processing models for computation and machine learning
This comprehensive course introduces the essential foundations of quantum computing, covering fundamental postulates of quantum mechanics and their application to quantum information processing. Students learn about gate-based quantum computing, error correction, adiabatic quantum computing, quantum simulation, and quantum machine learning. The course provides a physical and intuitive understanding while requiring minimal engineering prerequisites.
Instructors:
English
English
What you'll learn
Understand fundamental postulates of quantum mechanics
Master gate-based quantum computing principles
Learn quantum error correction techniques
Explore adiabatic quantum computing methods
Grasp quantum simulation concepts
Apply quantum principles to machine learning
Skills you'll gain
This course includes:
Live video
Graded assignments,Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
The course provides a foundational understanding of quantum computing principles and their practical applications. Students learn about the fundamental postulates of quantum mechanics and their mapping to quantum information processing models. The curriculum covers essential topics including gate-based quantum computing, error correction mechanisms, adiabatic quantum computing, quantum simulation techniques, and applications in machine learning. Special emphasis is placed on developing both theoretical knowledge and practical understanding.
Fee Structure
Instructor

7 Courses
Pioneering Spintronics and Quantum Physics Researcher
Pramey Upadhyaya is a distinguished Associate Professor of Electrical and Computer Engineering at Purdue University, whose academic journey includes a B.Tech from IIT Kharagpur (2009), followed by MS (2011) and Ph.D. (2015) degrees from UCLA, where he worked as a resident theorist in Prof. Kang Wang's Device Research Laboratory. Following his doctoral studies, he completed a postdoctoral fellowship at UCLA's Physics and Astronomy Department under Prof. Yaroslav Tserkovnyak. His groundbreaking research in quantum physics and spintronics has led to pioneering demonstrations of current-induced room-temperature skyrmion manipulations, spin torque switching using topological surface states, and NV-center probing of spin-caloritronics. His work has resulted in over 30 publications in prestigious journals including Science, Physical Review Letters, and Nature Nanotechnology, achieving an H-index of 24. His exceptional contributions to the field have been recognized with several prestigious awards, including an NSF Career Award (2020), Qualcomm Innovation Fellowship (2013), and Intel Summer Fellowship (2011), while his current research at Purdue focuses on advancing quantum phenomena and material science for next-generation computing applications.
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