Items filtered by date: Tuesday, 17 October 2023
Randomized tensor-network algorithms for random data in high-dimensions
Speaker: Yuehaw Khoo (University of Chicago)
Tensor-network ansatz has long been employed to solve the high-dimensional Schrödinger equation, demonstrating linear complexity scaling with respect to dimensionality. Recently, this ansatz has found applications in various machine learning scenarios, including supervised learning and generative modeling, where the data originates from a random process. In this talk, we present a new perspective on randomized linear algebra, showcasing its usage in estimating a density as a tensor-network from i.i.d. samples of a distribution, without the curse of dimensionality, and without the use of optimization techniques. Moreover, we illustrate how this concept can combine the strengths of particle and tensor-network methods for solving high-dimensional PDEs, resulting in enhanced flexibility for both approaches.
Time: December 1, 2023 3:40pm-4:40pm
Location: LeConte 440
Host: Wuchen Li
Hybrid quantum classical algorithms
Speaker: Xiantao Li (Pennsylvania State University)
Quantum computing has recently emerged as a potential tool for large-scale scientific computing. In sharp contrast to their classical counterparts, quantum computers use qubits that can exist in superposition, potentially offering exponential speedup for many computational problems. Current quantum devices are noisy and error-prone, and in near term, a hybrid approach is more appropriate. I will discuss this hybrid framework using three examples: quantum machine learning, quantum algorithms for density-functional theory and quantum optimal control. In particular, this talk will outline how quantum algorithms can be interfaced with a classical method, the convergence properties and the overall complexity.
Time: November 3, 2023 2:30pm-3:30pm
Location: LeConte 440
Host: Yi Sun