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The process tensor framework to open quantum systems provides the most general description of multi-time correlations in non-Markovian quantum dynamics. A compressed representation of a process tensor in terms of matrix product operators…

Approaching the long-time dynamics of non-Markovian open quantum systems presents a challenging task if the bath is strongly coupled. Recent proposals address this problem through a representation of the so-called process tensor in terms of…

Quantum Physics · Physics 2024-05-20 Valentin Link , Hong-Hao Tu , Walter T. Strunz

Process tensor matrix product operators (PT-MPOs) enable numerically exact simulations for an unprecedentedly broad range of open quantum systems. By representing environment influences in MPO form, they can be efficiently compressed using…

Quantum Physics · Physics 2025-01-22 Moritz Cygorek , Erik M. Gauger

While several numerical techniques are available for predicting the dynamics of non-Markovian open quantum systems, most struggle with simulations for very long memory and propagation times, e.g., due to superlinear scaling with the number…

Quantum Physics · Physics 2025-05-01 Moritz Cygorek , Jonathan Keeling , Brendon W. Lovett , Erik M. Gauger

The large dimensionality of environments is the limiting factor in applying optimal control to open quantum systems beyond Markovian approximations. Multiple methods exist to simulate non-Markovian open systems which effectively reduce the…

Quantum Physics · Physics 2024-10-08 Carlos Ortega-Taberner , Eoin O'Neill , Eoin Butler , Gerald E. Fux , P. R. Eastham

We show how to learn structures of generic, non-Markovian, quantum stochastic processes using a tensor network based machine learning algorithm. We do this by representing the process as a matrix product operator (MPO) and train it with a…

Quantum Physics · Physics 2021-01-04 Chu Guo , Kavan Modi , Dario Poletti

In this work, we combine an established method for open quantum systems -- the time evolving density matrix using orthogonal polynomials algorithm (TEDOPA) -- with the transfer tensors formalism (TTM), a new tool for the analysis,…

Quantum Physics · Physics 2016-02-25 Robert Rosenbach , Javier Cerrillo , Susana F. Huelga , Jianshu Cao , Martin Bodo Plenio

Matrix Product States (MPS) and Operators (MPO) have been proven to be a powerful tool to study quantum many-body systems but are restricted to moderately entangled states as the number of parameters scales exponentially with the…

We describe a general-purpose computational toolkit for simulating open quantum systems, which provides numerically exact solutions for composites of zero-dimensional quantum systems that may be strongly coupled to multiple, quite general…

Quantum Physics · Physics 2024-10-03 Moritz Cygorek , Erik M. Gauger

Quantum computing is arguably one of the most revolutionary and disruptive technologies of this century. Due to the ever-increasing number of potential applications as well as the continuing rise in complexity, the development, simulation,…

Quantum Physics · Physics 2023-01-03 Patrick Gelß , Stefan Klus , Sebastian Knebel , Zarin Shakibaei , Sebastian Pokutta

Matrix product states (MPSs) and matrix product operators (MPOs) are fundamental tools in the study of quantum many-body systems, particularly in the context of tensor network methods such as Time-Evolving Block Decimation (TEBD). However,…

Strongly Correlated Electrons · Physics 2025-11-21 Alberto Giuseppe Catalano

Identification of the optimal quantum metrological protocols in realistic many particle quantum models is in general a challenge that cannot be efficiently addressed by the state-of-the-art numerical and analytical methods. Here we provide…

We propose an alternative to the infinite density-matrix renormalization approach for accessing quantum many-body states within a finite-size calculation that faithfully mimics the thermodynamic limit. Our method constructs environment…

Strongly Correlated Electrons · Physics 2025-12-10 Souta Shimozono , Chisa Hotta

Towards the efficient simulation of near-term quantum devices using tensor network states, we introduce an improved real-space parallelizable matrix-product state (MPS) compression method. This method enables efficient compression of all…

Quantum Physics · Physics 2024-09-02 Rong-Yang Sun , Tomonori Shirakawa , Seiji Yunoki

We present a method for describing the time evolution of many-body controlled quantum systems using matrix product operators (MPOs). Existing techniques for solving the time-dependent Schr\"odinger equation (TDSE) with an MPO Hamiltonian…

Quantum Physics · Physics 2026-01-05 Llorenç Balada Gaggioli , Jakub Mareček

We present and test a protocol to learn the matrix-product operator (MPO) representation of an experimentally prepared quantum state. The protocol takes as an input classical shadows corresponding to local randomized measurements, and…

We introduce a matrix product operator (MPO) encoding of the Magnus expansion and the Dyson series for one-dimensional quantum lattice models with time-dependent Hamiltonians. The MPO construction can be made accurate up to arbitrary order…

Accurately simulating the non-Markovian dynamics of open quantum systems remains a significant challenge. While the recently proposed time-evolving matrix product operator (TEMPO) algorithm based on path integrals successfully circumvents…

Chemical Physics · Physics 2026-02-17 Xiaoyu Yang , Limin Liu , Wencheng Zhao , Jiajun Ren , Wei-Hai Fang

In the path integral formulation of the evolution of an open quantum system coupled to a Gaussian, non-interacting environment, the dynamical contribution of the latter is encoded in an object called the influence functional. Here, we…

Quantum Physics · Physics 2019-12-16 Mathias R. Jørgensen , Felix A. Pollock

The dynamics of one-dimensional quantum many-body systems is often numerically simulated with matrix-product states (MPSs). The computational complexity of MPS methods is known to be related to the growth of entropies of reduced density…

Quantum Physics · Physics 2023-08-08 Guillermo Preisser , David Wellnitz , Thomas Botzung , Johannes Schachenmayer
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