
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


Ryan Sweke - Should we use parameterized quantum circuits for machine learning? - IPAM at UCLA

Zoe Holmes - Exponential Concentration in Quantum Generative Modeling and Quantum Kernel Methods

Marco Cerezo - A Unified Theory of Barren Plateaus for Deep Parametrized Quantum Circuits

Matthias Caro - Classical Verification of Quantum Learning - IPAM at UCLA

Jens Eisert - Do quantum computers have application in machine learning & combinatorial optimization

Learning of neural networks w/ quantum computers & learning of quantum states with graphical models

Yihui Quek - Signal and noise: learning with random quantum circuits and other agents of chaos

Juan Carrasquilla - Training Binary Neural Networks in Quantum Superposition - IPAM at UCLA

Maria Schuld - How to rethink quantum machine learning - IPAM at UCLA

Nathan Wiebe - Quantum Machine Learning - IPAM at UCLA

Ruizhe Zhang - Quantum Speedups of Continuous Sampling and Optimization Problems - IPAM at UCLA

Jin Peng Liu - Provably Efficient Quantum Algorithms for Nonlinear Dynamics and Machine Learning

Konstantina Trivisa - Efficient Quantum algorithms for linear and non-linear differential equations

Jingbo Wang - Quantum walk, efficient implementation, and potential application - IPAM at UCLA

Andrew Baczewski - Quantum computation of stopping power for inertial fusion target design

Di Fang - Time-marching strategy can work quantumly for differential equations - IPAM at UCLA

Spring 2024 Program "Geometry, Statistical Mechanics, and Integrability" IPAM at UCLA

Alexander Kemper - Quantum algorithms for dynamics and dynamical observables - IPAM at UCLA

Chao Yang - An Efficient Block Encoding Quantum Circuit for a Pairing Hamiltonian - IPAM at UCLA
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