标题： 连续随机压缩：一种用于压缩MPO-MPS乘积的随机算法 显示英文标题

标题： Successive randomized compression: A randomized algorithm for the compressed MPO-MPS product

摘要： 张量网络如矩阵乘积态（MPSs）和矩阵乘积算子（MPOs）是表示指数级大的状态和算子的强大工具，应用于量子多体物理、机器学习、数值分析和其他领域。 在这些应用中，计算MPO-MPS乘积的压缩表示是一个基本的计算原语。 对于此操作，本文介绍了一种新的单次传递、随机算法，称为连续随机压缩（SRC），它在速度或准确性方面优于现有方法。 新算法的性能在合成问题和量子自旋系统的单位时间演化问题上进行了评估。 显示英文摘要

摘要： Tensor networks like matrix product states (MPSs) and matrix product operators (MPOs) are powerful tools for representing exponentially large states and operators, with applications in quantum many-body physics, machine learning, numerical analysis, and other areas. In these applications, computing a compressed representation of the MPO--MPS product is a fundamental computational primitive. For this operation, this paper introduces a new single-pass, randomized algorithm, called successive randomized compression (SRC), that improves on existing approaches in speed or in accuracy. The performance of the new algorithm is evaluated on synthetic problems and unitary time evolution problems for quantum spin systems.