
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


Nick Mayhall - Electronic structure theory via Classical and Quantum Computation - IPAM at UCLA

Prineha Narang - From nonequilibrium quantum systems to open quantum systems - IPAM at UCLA

Itay Hen - QMC of everything: Universal algorithm for simulating arbitrary quantum many-body systems

Paul Cazeaux - Randomized Algorithms for Rounding and Rank Compression in the Tensor Train Format

Shankar Balasubramanian - Ultraslow dynamics, fragile fragmentation, exotic entanglement

Ojas Parekh - Quantum Approximation Algorithms - IPAM at UCLA

Abhinav Kandala - Evidence for the utility of quantum computing before fault tolerance - IPAM UCLA

Sophia Economou - Adaptive quantum simulation algorithms - IPAM at UCLA

Jarrod McClean - The role of data, precomputation, and communication in a quantum learning landscape

Amira Abbas - On quantum backpropagation and information reuse - IPAM at UCLA

Roger Melko - Language Models for Quantum Simulation - IPAM at UCLA

Carlos Bravo Prieto - Understanding quantum machine learning also requires rethinking generalization

Vedran Dunjko - Exponential separations between classical and quantum learners - IPAM at UCLA

Louis Schatzki - Quantum Statistical Query Learning II of II - IPAM at UCLA

Vojtěch Havlíček - Quantum Statistical Query Learning I of II - IPAM at UCLA

Tongyang Li - On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks

Marika Maria Kieferova - Generating Approx. Ground State of Molecules Using Quantum Machine Learning

Hsin-Yuan (Robert) Huang - Learning to predict arbitrary quantum processes - IPAM at UCLA

Daniel Liang - Learning Beyond Stabilizer States - IPAM at UCLA
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