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PHYS-463: Computational quantum physics

Summary

The numerical simulation of quantum systems plays a central role in modern physics. This course gives an introduction to key simulation approaches, through lectures and practical programming exercises. Simulation methods based both on classical and quantum computers will be presented.

Lectures

Instructor

Giuseppe Carleo

Giuseppe Carleo is a computational quantum physicist, whose main focus is the development of advanced numerical algorithms tostudy challenging problems involving strongly interacting quantum systems.He is best known for the introduction of machine learning techniques to study both equilibrium and dynamical properties,based on a neural-network representations of quantum states, as well for the time-dependent variational Monte Carlo method.He earned a Ph.D. in Condensed Matter Theory from the International School for Advanced Studies (SISSA) in Italy in 2011.He held postdoctoral positions at the Institut d’Optique in France and ETH Zurich in Switzerland, where he alsoserved as a lecturer in computational quantum physics.In 2018, he joined the Flatiron Institute in New York City in 2018 at the Center for Computational Quantum Physics (CCQ), working as a Research Scientist and project leader, and also leading the development of the open-source project NetKet.Since September 2020 he is an assistant professor at EPFL, in Switzerland, leading the Computational Quantum Science Laboratory (CQSL).

Official source

https://edu.epfl.ch/coursebook/en/computational-quantum-physics-PHYS-463

Moodle Page

http://go.epfl.ch/PHYS-463

Summary

Lectures

Instructor

Giuseppe Carleo

Official source

https://edu.epfl.ch/coursebook/en/computational-quantum-physics-PHYS-463

Moodle Page

http://go.epfl.ch/PHYS-463

Ontological neighbourhood

Theoretical computer science

Theory of computation: Quantum computing

Lectures in this course (14)

Computational Quantum Physics

Covers numerical solutions to Schrödinger equations, quantum Monte Carlo simulations, tensor networks, quantum algorithms, and machine-learning approaches.

Computational Quantum Physics

Explores computational methods in quantum physics, emphasizing exact diagonalization and space discretization techniques.

Computational Quantum Physics

Covers path-integral ground state methods and Monte Carlo simulations in computational quantum physics.

Computational Quantum Physics: Path-Integral Monte Carlo

Explores Path-Integral Monte Carlo methods for discrete quantum models and ground state properties.

Computational Quantum Physics

Introduces quantum computing basics, covering qubits, gates, circuits, and simulations.

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