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AI in quantum science and technology (July 2026)
This short course explores the interplay between quantum science and artificial intelligence (AI). On one hand, modern AI techniques are becoming powerful tools for advancing quantum science, enabling efficient data analysis in quantum mechanics, quantum state reconstruction, and intelligent control of quantum experiments. On the other hand, quantum mechanical systems themselves offer new paradigms for information processing, inspiring the development of AI models based on quantum mechanical systems with the potential for significant computational advantages. Through a combination of conceptual discussions and practical demonstrations, the course introduces participants to how AI can enrich quantum physics and how quantum principles can enhance the capabilities of next-generation AI technologies. (Summer Camp 2026)
Academic Year 2026-27, Term 2
Classical Mechanics and Special Relativity (Classical Mechanics II)
This course offers an advanced view of classical mechanics through the frameworks of Lagrangian and Hamiltonian formalisms. Topics include the principle of least action, canonical transformations, Poisson brackets, Hamilton–Jacobi theory, the formulation of continuous systems, and the special theory of relativity, emphasizing its connection to classical dynamics.
For lecture-notes and homework sets, contact at sanjibghosh@cuhk.edu.cn
Academic year 2024-2025-2026, Term 2
PHY3420: Quantum Mechanics and its applications II
This course provides an introduction to quantum mechanics focusing on its applications. The course introduces key approximation methods in quantum mechanics to solve complex problems. It will develop a solid understanding of perturbation theories, their formalism and scope of applications. Topics in scattering theory, quantum dynamics, and their real-world applications will be covered in detail. Additionally, the course delves into the properties of quantum states and the density matrix, emphasizing their significance in modern quantum applications.
For lecture-notes and homework sets, contact at sanjibghosh@cuhk.edu.cn
Conference lectures on my research
At Pittsburgh Quantum Institute
https://www.youtube.com/watch?v=nth6FAIfTa0
Quantum reservoir computing for machine learning
https://www.youtube.com/watch?v=67Gj5NWQoEM
Quantum transport
https://www.youtube.com/watch?v=GBJkpVVd_Cc
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