
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


Victor Batista - Quantum Simulation for Chemistry and Materials Science, Part 1 of 2

Zohreh Davoudi - Quantum Simulation for Quantum Field Theories, Part 2 of 2

Zohreh Davoudi - Quantum Simulation for Quantum Field Theories, Part 1 of 2

Xiaodi Wu - Hamiltonian-oriented Quantum Algorithm Design and Implementation, Part 2 of 2

Xiaodi Wu - Hamiltonian-oriented Quantum Algorithm Design and Implementation, Part 1 of 2

Ken Brown - Tutorial Introduction to Quantum Simulation, Part 2 of 2 - IPAM at UCLA

Ken Brown - Tutorial Introduction to Quantum Simulation, Part 1 of 2 - IPAM at UCLA

Kristan Temme - Szegedy Walk Unitaries for Quantum Maps - IPAM at UCLA

Chunhao Wang - Quantum Regression Theory, Efficient Computation Response, Non-Markovian Open Systems

Yihui Quek - Hamiltonian Decoded Quantum Interferometry - IPAM at UCLA

Di Fang - Mathematical Analysis of Many-Body Quantum Simulation with Coulomb Potentials

Xiantao Li - A general dilation framework for non-Hermitian dynamics simulations - IPAM at UCLA

Thiago Bergamaschi - A Structural Theory of Quantum Metastability: Markov Properties and Area Laws

M. Isabel Franco Garrido - Optimization algorithms using Gibbs state preparation and beyond

Pawel Wocjan - Quantized Markov chain couplings that prepare Qsamples - IPAM at UCLA

Daniel Stilck Franca - Learning and certification of local time-dependent quantum dynamics and noise

Jianfeng Lu - Lifting of Quantum Markov Processes - IPAM at UCLA

Cambyse Rouze - Heisenberg-limited Hamiltonian learning continuous variable systems via dissipation

Alexander Zlokapa - Average-case quantum complexity from glassiness - IPAM at UCLA
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