Successive randomized compression: A randomized algorithm for the compressed MPO-MPS product
Abstract
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.
Fields of Study & Subjects
Quantum many-body systems
Quantum Computing Algorithms and Architecture
Tensor decomposition and applications
Publication details
- Journal / source
- Quantum
- Publisher
- Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften
- Volume
- 10
- Pages
- 2022–2022
- Publication date
- 10 March 2026
- Language
- EN