Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Physics in Higher Education

Tensor Networks in Many-body Quantum Control Problems

Maxim A. Gavreev, I. A. Luchnikov, Aleksey K. Fedorov

Published Jan 1, 2023• 0 citations
Publisher
DOI
10.54965/16093143_2023_29_s1_14

Related papers

Many-body control with reinforcement learning and tensor networks

Ying Lü, Shi-Ju Ran

Nature Machine Intelligence•Oct 4, 2023•4 citations

Tensor networks for complex quantum systems

Román Orús

arXiv:1812.04011•Nature Reviews Physics•Aug 5, 2019•628 citations

Size consistency of tensor network methods for quantum many-body systems

Zhen Wang, Yong‐Jian Han, Guang‐Can Guo +1 more

Recently developed tensor network methods demonstrate great potential for addressing the quantum many-body problem, by constructing variational spaces with polynomially, instead of exponentially, scaled parameters. Constructing such an efficient tensor network, and thus the variational space, is a subtle problem and the main obstacle of the method. We demonstrate the necessity of size consistency in tensor network methods for their success in addressing the quantum many-body problem. We further demonstrate that size consistency is independent of the entanglement criterion, thus providing a general and tight constraint to construct the tensor network method. We propose a general and easy rule to construct a size-consistent tensor network.

arXiv:1304.6150•Physical Review B•Sep 17, 2013•6 citations

Functional tensor network solving many-body Schrödinger equation

Rui Hong, Ya-Xuan Xiao, Jie Hu +2 more

Solving the many-body Schr\"odinger equation in continuous spaces with the presence of strong correlations is an extremely important and challenging issue in quantum physics. In this work, we propose the functional tensor network (FTN) approach to solve the many-body Schr\"odinger equation. Provided the orthonormal functional bases, we represent the coefficients of the many-body wave function as a tensor network. The observables, such as energy, can be calculated simply by tensor contractions. Simulating the ground state becomes solving a minimization problem defined by the tensor network. An efficient gradient-decent algorithm based on automatically differentiable tensors is proposed. We here take the matrix product state (MPS), whose complexity scales only linearly with the system size, as an example. We apply our approach to solve the ground state of coupled harmonic oscillators and achieve high accuracy by comparing our results with the exact solutions. Reliable results are also given in the presence of three-body interactions, where the system cannot be decoupled from isolated oscillators. Our approach is simple and, with well-controlled error, essentially different from highly nonlinear neural-network solvers. Our work extends the applications of the tensor network from quantum lattice models to systems in continuous space. The FTN can be used as a general solver of the differential equations with many variables. The MPS exemplified here can be generalized to, e.g., fermionic tensor networks to solve the electronic Schr\"odinger equation.

arXiv:2201.12823•Physical review. B./Physical review. B•Apr 12, 2022•12 citations

Tensor Product Approach to Quantum Control

Diego Quiñones, Sergey Dolgov, Dmitry Savostyanov

arXiv:1903.00064•Springer eBooks•Jan 1, 2019•3 citations

Quantum Manybody Systems, Tensor Networks and Neural Networks

Akinori Tanaka, Akio Tomiya, Koji Hashimoto

Mathematical physics studies•Jan 1, 2021•0 citations
Recently developed tensor network methods demonstrate great potential for addressing the quantum many-body problem, by constructing variational spaces with polynomially, instead of exponentially, scaled parameters. Constructing such an efficient tensor network, and thus the variational space, is a subtle problem and the main obstacle of the method. We demonstrate the necessity of size consistency in tensor network methods for their success in addressing the quantum many-body problem. We further demonstrate that size consistency is independent of the entanglement criterion, thus providing a general and tight constraint to construct the tensor network method. We propose a general and easy rule to construct a size-consistent tensor network.
Solving the many-body Schr\"odinger equation in continuous spaces with the presence of strong correlations is an extremely important and challenging issue in quantum physics. In this work, we propose the functional tensor network (FTN) approach to solve the many-body Schr\"odinger equation. Provided the orthonormal functional bases, we represent the coefficients of the many-body wave function as a tensor network. The observables, such as energy, can be calculated simply by tensor contractions. Simulating the ground state becomes solving a minimization problem defined by the tensor network. An efficient gradient-decent algorithm based on automatically differentiable tensors is proposed. We here take the matrix product state (MPS), whose complexity scales only linearly with the system size, as an example. We apply our approach to solve the ground state of coupled harmonic oscillators and achieve high accuracy by comparing our results with the exact solutions. Reliable results are also given in the presence of three-body interactions, where the system cannot be decoupled from isolated oscillators. Our approach is simple and, with well-controlled error, essentially different from highly nonlinear neural-network solvers. Our work extends the applications of the tensor network from quantum lattice models to systems in continuous space. The FTN can be used as a general solver of the differential equations with many variables. The MPS exemplified here can be generalized to, e.g., fermionic tensor networks to solve the electronic Schr\"odinger equation.