应6163银河net163am赵继泽教授邀请，中国科学院物理研究所廖海军副研究员近日来访我校并做学术报告。欢迎广大师生届时参加！

题    目：Differentiable Programming Tensor Networks

报告人：廖海军 副研究员

时    间：2020年11月13日（周五）上午10:00

地    点：理工楼1215报告厅

联系人：赵继泽

摘    要：Differentiable programming is a fresh programming paradigm which composes parameterized algorithmic components and optimizes them using gradient search. The concept emerges from deep learning but is not limited to training neural networks. We present the theory and practice of programming tensor network algorithms in a fully differentiable way. By formulating the tensor network algorithm as a computation graph, one can compute higher-order derivatives of the program accurately and efficiently using automatic differentiation. We present essential techniques to differentiate through the tensor networks contraction algorithms, including numerical stable differentiation for tensor decompositions and efficient backpropagation through fixed-point iterations. As a demonstration, we compute the specific heat of the Ising model directly by taking the second-order derivative of the free energy obtained in the tensor renormalization group calculation. Next, we perform gradient-based variational optimization of infinite projected entangled pair states for the quantum antiferromagnetic Heisenberg model and obtain state-of- the-art variational energy and magnetization with moderate efforts. Differentiable programming removes laborious human efforts in deriving and implementing analytical gradients for tensor network programs, which opens the door to more innovations in tensor network algorithms and applications.

个人简介：

廖海军， 2009年中国人民大学本科毕业，2014年中国人民大学博士毕业，2014年至2017年在中国科学院物理研究所从事博士后，2017年至今在中科院物理研究所担任副研究员。主要专注于量子多体计算方法的发展与应用。