Simulating the Quantum World with Data-Free, Physics-Driven Machine Learning

  • Speaker
  • Portrait photo of Giuseppe CarleoGiuseppe Carleo, Ph.D.Assistant Professor, École Polytechnique
    Research Scientist, CCQ (2018-2020), Flatiron Institute
Date & Time


Location

Gerald D. Fischbach Auditorium
160 5th Ave
New York, NY 10010 United States

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Doors open: 5:30 p.m. (No entrance before 5:30 p.m.)

Lecture: 6:00 p.m. – 7:00 p.m. (Admittance closes at 6:20 p.m.)

The 2024 lecture series in mathematics and computer science is “Machine Learning in the Natural Sciences.” Machine learning has become a transformative tool for advancing science. In these lectures, scientists will discuss their use of machine learning in everything from biology and oceanography to astrophysics and particle physics. These applications are sparking discoveries while also helping scientists uncover what the tools are actually gleaning from data.

2024 Lecture Series Themes

Biology: Dynamics of Life

Mathematics and Computer Science: Machine Learning in the Natural Sciences

Neuroscience and Autism Science: The Social Brain

Physics: Atmospheres: Earth to Exoplanets

About Presidential Lectures

Presidential Lectures are free public colloquia centered on four main themes: Biology, Physics, Mathematics and Computer Science, and Neuroscience and Autism Science. These curated, high-level scientific talks feature leading scientists and mathematicians and are intended to foster discourse and drive discovery among the broader NYC-area research community. We invite those interested in the topic to join us for this weekly lecture series.

The behavior of electrons is chiefly responsible for the properties of materials and molecules. Predicting the behavior of many interacting electrons poses a significant scientific challenge and has led to the development of many methods of tackling problems in quantum many-body physics.

In this lecture, Giuseppe Carleo will focus on simulation-driven machine learning techniques. He’ll explore how artificial neural networks can represent quantum states, offering a powerful alternative to traditional variational methods. The talk will introduce how these approaches systematically and controllably learn many-body wave functions without relying on pre-existing data. He’ll examine applications in diverse domains, including condensed matter, chemistry and nuclear physics. Special attention will be given to how neural network representations have advanced our ability to simulate prototypical many-body quantum systems, surpassing previous variational descriptions.

About the Speaker

Portrait photo of Giuseppe Carleo

Carleo is a professor of physics at the Swiss Federal Institute of Technology Lausanne (EPFL), where he leads the Computational Quantum Science Laboratory. He earned his Ph.D. from the International School for Advanced Studies (SISSA) in Italy in 2011 and held postdoctoral positions at the Institut d’Optique in France and ETH Zurich in Switzerland. From 2018 to 2020, he was a research scientist at the Flatiron Institute’s Center for Computational Quantum Physics in New York City. Carleo’s expertise is in developing advanced numerical algorithms for studying strongly interacting quantum systems. He pioneered the application of machine learning techniques to quantum physics, introducing neural network representations of quantum states. Carleo is also known for developing the time-dependent variational Monte Carlo method and leading the open-source project NetKet.

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