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Institute for Pure & Applied Mathematics (IPAM) ยท Science

Institute for Pure & Applied Mathematics (IPAM)

The Institute for Pure and Applied Mathematics (IPAM) is a National Science Foundation Math Institute at the University of California, Los Angeles (UCLA) that fosters the interaction of mathematics with a broad range of science and technology, builds new interdisciplinary research communities, promotes mathematical innovation, and engages and transforms the world through mathematics. IPAM fulfills its mission through workshops and programs that connect mathematics and other disciplines or multiple areas of mathematics. These activities bring in thousands of visitors annually from academia, government, and industry. The NSF has encouraged IPAM to make the scientific presentations of our speakers available to a wider audience. While this YouTube channel contains many of our most popular videos, our full video archive is available at www.ipam.ucla.edu/videos.

Episodes

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Srinivasan Arunachalam - Overview of learning structured quantum states - IPAM at UCLA

18 Oct 2023
54m
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Ryan Sweke - Should we use parameterized quantum circuits for machine learning? - IPAM at UCLA

18 Oct 2023
59m
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Zoe Holmes - Exponential Concentration in Quantum Generative Modeling and Quantum Kernel Methods

18 Oct 2023
59m
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Marco Cerezo - A Unified Theory of Barren Plateaus for Deep Parametrized Quantum Circuits

18 Oct 2023
46m
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Matthias Caro - Classical Verification of Quantum Learning - IPAM at UCLA

17 Oct 2023
52m
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Jens Eisert - Do quantum computers have application in machine learning & combinatorial optimization

17 Oct 2023
51m
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Learning of neural networks w/ quantum computers & learning of quantum states with graphical models

17 Oct 2023
51m
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Yihui Quek - Signal and noise: learning with random quantum circuits and other agents of chaos

16 Oct 2023
38m
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Juan Carrasquilla - Training Binary Neural Networks in Quantum Superposition - IPAM at UCLA

16 Oct 2023
50m
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Maria Schuld - How to rethink quantum machine learning - IPAM at UCLA

16 Oct 2023
43m
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Nathan Wiebe - Quantum Machine Learning - IPAM at UCLA

16 Oct 2023
1h 1m
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Ruizhe Zhang - Quantum Speedups of Continuous Sampling and Optimization Problems - IPAM at UCLA

06 Oct 2023
55m
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Jin Peng Liu - Provably Efficient Quantum Algorithms for Nonlinear Dynamics and Machine Learning

06 Oct 2023
54m
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Konstantina Trivisa - Efficient Quantum algorithms for linear and non-linear differential equations

06 Oct 2023
53m
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Jingbo Wang - Quantum walk, efficient implementation, and potential application - IPAM at UCLA

06 Oct 2023
51m
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Andrew Baczewski - Quantum computation of stopping power for inertial fusion target design

06 Oct 2023
53m
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Di Fang - Time-marching strategy can work quantumly for differential equations - IPAM at UCLA

06 Oct 2023
48m
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Spring 2024 Program "Geometry, Statistical Mechanics, and Integrability" IPAM at UCLA

05 Oct 2023
33m
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Alexander Kemper - Quantum algorithms for dynamics and dynamical observables - IPAM at UCLA

05 Oct 2023
54m
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Chao Yang - An Efficient Block Encoding Quantum Circuit for a Pairing Hamiltonian - IPAM at UCLA

05 Oct 2023
39m
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