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Institute for Pure & Applied Mathematics (IPAM) · 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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Joonho Lee - Auxiliary-Field Quantum Monte Carlo Beyond Hartree-Fock Trial Wavefunctions

08 Nov 2023
59m
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Nick Mayhall - Electronic structure theory via Classical and Quantum Computation - IPAM at UCLA

08 Nov 2023
59m
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Prineha Narang - From nonequilibrium quantum systems to open quantum systems - IPAM at UCLA

08 Nov 2023
52m
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Itay Hen - QMC of everything: Universal algorithm for simulating arbitrary quantum many-body systems

07 Nov 2023
53m
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Paul Cazeaux - Randomized Algorithms for Rounding and Rank Compression in the Tensor Train Format

07 Nov 2023
40m
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Shankar Balasubramanian - Ultraslow dynamics, fragile fragmentation, exotic entanglement

07 Nov 2023
56m
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Ojas Parekh - Quantum Approximation Algorithms - IPAM at UCLA

06 Nov 2023
55m
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Abhinav Kandala - Evidence for the utility of quantum computing before fault tolerance - IPAM UCLA

06 Nov 2023
1h 0m
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Sophia Economou - Adaptive quantum simulation algorithms - IPAM at UCLA

06 Nov 2023
1h 1m
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Jarrod McClean - The role of data, precomputation, and communication in a quantum learning landscape

20 Oct 2023
48m
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Amira Abbas - On quantum backpropagation and information reuse - IPAM at UCLA

20 Oct 2023
35m
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Roger Melko - Language Models for Quantum Simulation - IPAM at UCLA

20 Oct 2023
59m
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Carlos Bravo Prieto - Understanding quantum machine learning also requires rethinking generalization

19 Oct 2023
43m
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Vedran Dunjko - Exponential separations between classical and quantum learners - IPAM at UCLA

19 Oct 2023
51m
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Louis Schatzki - Quantum Statistical Query Learning II of II - IPAM at UCLA

19 Oct 2023
44m
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Vojtěch Havlíček - Quantum Statistical Query Learning I of II - IPAM at UCLA

19 Oct 2023
45m
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Tongyang Li - On Quantum Speedups for Nonconvex Optimization via Quantum Tunneling Walks

19 Oct 2023
55m
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Marika Maria Kieferova - Generating Approx. Ground State of Molecules Using Quantum Machine Learning

19 Oct 2023
51m
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Hsin-Yuan (Robert) Huang - Learning to predict arbitrary quantum processes - IPAM at UCLA

18 Oct 2023
50m
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Daniel Liang - Learning Beyond Stabilizer States - IPAM at UCLA

18 Oct 2023
46m
Page 46

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