Three Lagrangians for the complete-active space coupled-cluster method
- URL: http://arxiv.org/abs/2205.08792v2
- Date: Sat, 10 Jun 2023 14:27:03 GMT
- Title: Three Lagrangians for the complete-active space coupled-cluster method
- Authors: Simen Kvaal
- Abstract summary: Three fully variational formulations of the complete-active space coupled-cluster (CASCC) method are derived.
Model vectors of matrix-product states are considered, and it is argued that the present variational formulation allows favorably-scaling coupled-cluster calculations.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Three fully variational formulations of the complete-active space
coupled-cluster (CASCC) method are derived. The formulations include the
ability to approximate the model vectors by smooth manifolds, thereby opening
up the possibility for overcoming the exponential wall of scaling for model
spaces of CAS type. In particular, model vectors of matrix-product states are
considered, and it is argued that the present variational formulation allows
not only favorably-scaling multireference coupled-cluster calculations, but
also systematic correction of tailored coupled-cluster calculation and of
quantum chemical density-matrix renormalization group methods, which are fast
and polynomial scaling, but lacks the ability to properly resolve dynamical
correlation at chemical accuracy. The extension of the variational formulations
to the time-domain is also discussed, with derivations of abstract evolution
equations.
Related papers
- Renormalized Internally-Contracted Multireference Coupled Cluster with Perturbative Triples [0.0]
We combine the many-body formulation of the internally contracted multireference coupled cluster (ic-MRCC) method with Evangelista's multireference formulation of the driven similarity renormalization group (DSRG)
We denote the new approach, the renormalized ic-MRCC (ric-MRCC) method.
We demonstrate the accuracy of our approaches in comparison to advanced multireference methods for the potential energy curves of H8, F2, H2O, N2, and Cr2.
arXiv Detail & Related papers (2024-05-25T09:15:41Z) - Applications of flow models to the generation of correlated lattice QCD ensembles [69.18453821764075]
Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters.
This work demonstrates how these correlations can be exploited for variance reduction in the computation of observables.
arXiv Detail & Related papers (2024-01-19T18:33:52Z) - Variational manifolds for ground states and scarred dynamics of blockade-constrained spin models on two and three dimensional lattices [0.0]
We introduce a variational manifold of simple tensor network states for the study of a family of constrained models that describe spin-1/2 systems.
Our method can be interpreted as a generalization of mean-field theory to constrained spin models.
arXiv Detail & Related papers (2023-11-15T13:52:21Z) - Calculating non-linear response functions for multi-dimensional
electronic spectroscopy using dyadic non-Markovian quantum state diffusion [68.8204255655161]
We present a methodology for simulating multi-dimensional electronic spectra of molecular aggregates with coupling electronic excitation to a structured environment.
A crucial aspect of our approach is that we propagate the NMQSD equation in a doubled system Hilbert space but with the same noise.
arXiv Detail & Related papers (2022-07-06T15:30:38Z) - Improved modeling of dynamic quantum systems using exact Lindblad master
equations [0.22940141855172028]
Correcting statistical assumptions provides access to the exact form of the exchange interaction.
The exact form of the interaction is only different from the traditional equation by a scalar correction factor.
arXiv Detail & Related papers (2022-05-10T19:55:57Z) - Equivariant Diffusion for Molecule Generation in 3D [74.289191525633]
This work introduces a diffusion model for molecule computation generation in 3D that is equivariant to Euclidean transformations.
Experimentally, the proposed method significantly outperforms previous 3D molecular generative methods regarding the quality of generated samples and efficiency at training time.
arXiv Detail & Related papers (2022-03-31T12:52:25Z) - A deep learning driven pseudospectral PCE based FFT homogenization
algorithm for complex microstructures [68.8204255655161]
It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
arXiv Detail & Related papers (2021-10-26T07:02:14Z) - The Connection between Discrete- and Continuous-Time Descriptions of
Gaussian Continuous Processes [60.35125735474386]
We show that discretizations yielding consistent estimators have the property of invariance under coarse-graining'
This result explains why combining differencing schemes for derivatives reconstruction and local-in-time inference approaches does not work for time series analysis of second or higher order differential equations.
arXiv Detail & Related papers (2021-01-16T17:11:02Z) - Tensor lattice field theory with applications to the renormalization
group and quantum computing [0.0]
We discuss the successes and limitations of statistical sampling for a sequence of models studied in the context of lattice QCD.
We show that these lattice models can be reformulated using tensorial methods where the field integrations in the path-integral formalism are replaced by discrete sums.
We derive Hamiltonians suitable to perform quantum simulation experiments, for instance using cold atoms, or to be programmed on existing quantum computers.
arXiv Detail & Related papers (2020-10-13T16:46:34Z) - Stochastic spectral embedding [0.0]
We propose a novel sequential adaptive surrogate modeling method based on "stochastic spectral embedding" (SSE)
We show how the method compares favorably against state-of-the-art sparse chaos expansions on a set of models with different complexity and input dimension.
arXiv Detail & Related papers (2020-04-09T11:00:07Z) - Inverse Learning of Symmetries [71.62109774068064]
We learn the symmetry transformation with a model consisting of two latent subspaces.
Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser.
Our model outperforms state-of-the-art methods on artificial and molecular datasets.
arXiv Detail & Related papers (2020-02-07T13:48:52Z)
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.