Comparative Study of State-of-the-Art Matrix-Product-State Methods for
Lattice Models with Large Local Hilbert Spaces
- URL: http://arxiv.org/abs/2011.07412v4
- Date: Thu, 30 Sep 2021 19:04:50 GMT
- Title: Comparative Study of State-of-the-Art Matrix-Product-State Methods for
Lattice Models with Large Local Hilbert Spaces
- Authors: Jan Stolpp, Thomas K\"ohler, Salvatore R. Manmana, Eric Jeckelmann,
Fabian Heidrich-Meisner and Sebastian Paeckel
- Abstract summary: matrix-product states (MPS) provide a flexible and generic ansatz class.
We describe and compare three state-of-the-art MPS methods each of which exploits a different approach to tackle the computational complexity.
We analyze the properties of these methods for the example of the Holstein model, performing high-precision calculations as well as a finite-size-scaling analysis of relevant ground-state obervables.
- Score: 0.06524460254566902
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Lattice models consisting of high-dimensional local degrees of freedom
without global particle-number conservation constitute an important problem
class in the field of strongly correlated quantum many-body systems. For
instance, they are realized in electron-phonon models, cavities, atom-molecule
resonance models, or superconductors. In general, these systems elude a
complete analytical treatment and need to be studied using numerical methods
where matrix-product states (MPS) provide a flexible and generic ansatz class.
Typically, MPS algorithms scale at least quadratic in the dimension of the
local Hilbert spaces. Hence, tailored methods, which truncate this dimension,
are required to allow for efficient simulations. Here, we describe and compare
three state-of-the-art MPS methods each of which exploits a different approach
to tackle the computational complexity. We analyze the properties of these
methods for the example of the Holstein model, performing high-precision
calculations as well as a finite-size-scaling analysis of relevant ground-state
obervables. The calculations are performed at different points in the phase
diagram yielding a comprehensive picture of the different approaches.
Related papers
- Uncertainty Quantification in Large Language Models Through Convex Hull Analysis [0.36832029288386137]
This study proposes a novel geometric approach to uncertainty quantification using convex hull analysis.
The proposed method leverages the spatial properties of response embeddings to measure the dispersion and variability of model outputs.
arXiv Detail & Related papers (2024-06-28T07:47:34Z) - Data-freeWeight Compress and Denoise for Large Language Models [101.53420111286952]
We propose a novel approach termed Data-free Joint Rank-k Approximation for compressing the parameter matrices.
We achieve a model pruning of 80% parameters while retaining 93.43% of the original performance without any calibration data.
arXiv Detail & Related papers (2024-02-26T05:51:47Z) - Gaussian Entanglement Measure: Applications to Multipartite Entanglement
of Graph States and Bosonic Field Theory [50.24983453990065]
An entanglement measure based on the Fubini-Study metric has been recently introduced by Cocchiarella and co-workers.
We present the Gaussian Entanglement Measure (GEM), a generalization of geometric entanglement measure for multimode Gaussian states.
By providing a computable multipartite entanglement measure for systems with a large number of degrees of freedom, we show that our definition can be used to obtain insights into a free bosonic field theory.
arXiv Detail & Related papers (2024-01-31T15:50:50Z) - Online Variational Sequential Monte Carlo [49.97673761305336]
We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference.
Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
arXiv Detail & Related papers (2023-12-19T21:45:38Z) - Weighted Riesz Particles [0.0]
We consider the target distribution as a mapping where the infinite-dimensional space of the parameters consists of a number of deterministic submanifolds.
We study the properties of the point, called Riesz, and embed it into sequential MCMC.
We find that there will be higher acceptance rates with fewer evaluations.
arXiv Detail & Related papers (2023-12-01T14:36:46Z) - Gaussian process regression and conditional Karhunen-Lo\'{e}ve models
for data assimilation in inverse problems [68.8204255655161]
We present a model inversion algorithm, CKLEMAP, for data assimilation and parameter estimation in partial differential equation models.
The CKLEMAP method provides better scalability compared to the standard MAP method.
arXiv Detail & Related papers (2023-01-26T18:14:12Z) - GANs and Closures: Micro-Macro Consistency in Multiscale Modeling [0.0]
We present an approach that couples physics-based simulations and biasing methods for sampling conditional distributions with Machine Learning-based conditional generative adversarial networks.
We show that this framework can improve multiscale SDE dynamical systems sampling, and even shows promise for systems of increasing complexity.
arXiv Detail & Related papers (2022-08-23T03:45:39Z) - Hybridized Methods for Quantum Simulation in the Interaction Picture [69.02115180674885]
We provide a framework that allows different simulation methods to be hybridized and thereby improve performance for interaction picture simulations.
Physical applications of these hybridized methods yield a gate complexity scaling as $log2 Lambda$ in the electric cutoff.
For the general problem of Hamiltonian simulation subject to dynamical constraints, these methods yield a query complexity independent of the penalty parameter $lambda$ used to impose an energy cost.
arXiv Detail & Related papers (2021-09-07T20:01:22Z) - A data-driven peridynamic continuum model for upscaling molecular
dynamics [3.1196544696082613]
We propose a learning framework to extract, from molecular dynamics data, an optimal Linear Peridynamic Solid model.
We provide sufficient well-posedness conditions for discretized LPS models with sign-changing influence functions.
This framework guarantees that the resulting model is mathematically well-posed, physically consistent, and that it generalizes well to settings that are different from the ones used during training.
arXiv Detail & Related papers (2021-08-04T07:07:47Z) - Generalized Matrix Factorization: efficient algorithms for fitting
generalized linear latent variable models to large data arrays [62.997667081978825]
Generalized Linear Latent Variable models (GLLVMs) generalize such factor models to non-Gaussian responses.
Current algorithms for estimating model parameters in GLLVMs require intensive computation and do not scale to large datasets.
We propose a new approach for fitting GLLVMs to high-dimensional datasets, based on approximating the model using penalized quasi-likelihood.
arXiv Detail & Related papers (2020-10-06T04:28:19Z) - Efficient and Flexible Approach to Simulate Low-Dimensional Quantum
Lattice Models with Large Local Hilbert Spaces [0.08594140167290096]
We introduce a mapping that allows to construct artificial $U(1)$ symmetries for any type of lattice model.
Exploiting the generated symmetries, numerical expenses that are related to the local degrees of freedom decrease significantly.
Our findings motivate an intuitive physical picture of the truncations occurring in typical algorithms.
arXiv Detail & Related papers (2020-08-19T14:13:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.