Density-potential inversion from Moreau-Yosida regularization
- URL: http://arxiv.org/abs/2212.12727v3
- Date: Sat, 1 Apr 2023 12:48:03 GMT
- Title: Density-potential inversion from Moreau-Yosida regularization
- Authors: Markus Penz, Mih\'aly A. Csirik, Andre Laestadius
- Abstract summary: Zhao-Morrison-Parr method is used to compute the effective potential that yields precisely that density.
We show how this and similar inversion procedures relate to the Moreau-Yosida regularization of density functionals on Banach spaces.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: For a quantum-mechanical many-electron system, given a density, the
Zhao-Morrison-Parr method allows to compute the effective potential that yields
precisely that density. In this work, we demonstrate how this and similar
inversion procedures mathematically relate to the Moreau-Yosida regularization
of density functionals on Banach spaces. It is shown that these inversion
procedures can in fact be understood as a limit process as the regularization
parameter approaches zero. This sheds new insight on the role of Moreau-Yosida
regularization in density-functional theory and allows to systematically
improve density-potential inversion. Our results apply to the Kohn-Sham setting
with fractional occupation that determines an effective one-body potential that
in turn reproduces an interacting density.
Related papers
- Characterizing Quantum Coherence via Schur-Horn Majorization: Degenerate Distillation and Refined Entropic Uncertainty [4.138060581023728]
We introduce a versatile suite of coherence measures that satisfy all resource theoretic axioms under incoherent operations.
This unifying approach clarifies the geometric boundaries of physically realizable states in von Neumann-Tsallis entropy space.
We strengthen the entropy-based uncertainty relation by refining the Massen-Uffink bound to account for the largest eigenvalues across distinct measurement bases.
arXiv Detail & Related papers (2025-03-12T06:51:11Z) - A Fokker-Planck-Based Loss Function that Bridges Dynamics with Density Estimation [1.8434042562191815]
We derive a novel loss function from the Fokker-Planck equation that links dynamical system models with their probability density functions.
For density estimation, we propose a density estimator that integrates a Gaussian Mixture Model with a normalizing flow model.
It is compatible with a variety of data-based training methodologies, including maximum likelihood and score matching.
arXiv Detail & Related papers (2025-02-24T22:27:25Z) - Highly Accurate Real-space Electron Densities with Neural Networks [7.176850154835262]
We introduce a novel method to obtain accurate densities from real-space many-electron wave functions.
We use variational quantum Monte Carlo with deep-learning ans"atze (deep QMC) to obtain highly accurate wave functions free of basis set errors.
arXiv Detail & Related papers (2024-09-02T14:56:22Z) - Density Estimation via Binless Multidimensional Integration [45.21975243399607]
We introduce the Binless Multidimensional Thermodynamic Integration (BMTI) method for nonparametric, robust, and data-efficient density estimation.
BMTI estimates the logarithm of the density by initially computing log-density differences between neighbouring data points.
The method is tested on a variety of complex synthetic high-dimensional datasets, and is benchmarked on realistic datasets from the chemical physics literature.
arXiv Detail & Related papers (2024-07-10T23:45:20Z) - Entanglement and localization in long-range quadratic Lindbladians [49.1574468325115]
Signatures of localization have been observed in condensed matter and cold atomic systems.
We propose a model of one-dimensional chain of non-interacting, spinless fermions coupled to a local ensemble of baths.
We show that the steady state of the system undergoes a localization entanglement phase transition by tuning $p$ which remains stable in the presence of coherent hopping.
arXiv Detail & Related papers (2023-03-13T12:45:25Z) - D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT)
We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity.
In addition, we show that our approach enables us to explore more complex neural-based wave functions.
arXiv Detail & Related papers (2023-03-01T10:38:10Z) - The Levy-Lieb embedding of density functional theory and its Quantum
Kernel: Illustration for the Hubbard Dimer using near-term quantum algorithms [0.0]
We numerically implement the Levy-Lieb procedure for a paradigmatic lattice system, the Hubbard dimer.
We demonstrate density variational minimization using the resulting hybrid quantum-classical scheme.
arXiv Detail & Related papers (2022-07-19T00:23:52Z) - Density-Based Clustering with Kernel Diffusion [59.4179549482505]
A naive density corresponding to the indicator function of a unit $d$-dimensional Euclidean ball is commonly used in density-based clustering algorithms.
We propose a new kernel diffusion density function, which is adaptive to data of varying local distributional characteristics and smoothness.
arXiv Detail & Related papers (2021-10-11T09:00:33Z) - Optical reconstruction of collective density matrix of qutrit [0.0]
Reconstruction of a quantum state is of prime importance for quantum-information science.
We present a method of reconstruction of a collective density matrix of an atomic ensemble, consisting of atoms with an $F=1$ ground state.
arXiv Detail & Related papers (2021-07-08T15:54:49Z) - Density-potential functional theory for fermions in one dimension [0.0]
orbital-free density-potential functional theory (DPFT) is a more flexible variant of Hohenberg-Kohn density functional theory.
DPFT is scalable, universally applicable in both position and momentum space, and allows kinetic and interaction energy to be approximated consistently.
The high quality of our results for Fermi gases in Morse potentials invites the use of DPFT for describing more exotic systems.
arXiv Detail & Related papers (2021-06-15T02:10:23Z) - Imitation with Neural Density Models [98.34503611309256]
We propose a new framework for Imitation Learning (IL) via density estimation of the expert's occupancy measure followed by Imitation Occupancy Entropy Reinforcement Learning (RL) using the density as a reward.
Our approach maximizes a non-adversarial model-free RL objective that provably lower bounds reverse Kullback-Leibler divergence between occupancy measures of the expert and imitator.
arXiv Detail & Related papers (2020-10-19T19:38:36Z) - Density profile of a semi-infinite one-dimensional Bose gas and bound
states of the impurity [62.997667081978825]
We study the effect of the boundary on a system of weakly interacting bosons in one dimension.
The quantum contribution to the boson density gives rise to small corrections of the bound state energy levels.
arXiv Detail & Related papers (2020-07-21T13:12:33Z) - The role of boundary conditions in quantum computations of scattering
observables [58.720142291102135]
Quantum computing may offer the opportunity to simulate strongly-interacting field theories, such as quantum chromodynamics, with physical time evolution.
As with present-day calculations, quantum computation strategies still require the restriction to a finite system size.
We quantify the volume effects for various $1+1$D Minkowski-signature quantities and show that these can be a significant source of systematic uncertainty.
arXiv Detail & Related papers (2020-07-01T17:43:11Z) - Variational-Correlations Approach to Quantum Many-body Problems [1.9336815376402714]
We investigate an approach for studying the ground state of a quantum many-body Hamiltonian.
The challenge set by the exponentially-large Hilbert space is circumvented by approximating the positivity of the density matrix.
We demonstrate the ability of this approach to produce long-range correlations, and a ground-state energy that converges to the exact result.
arXiv Detail & Related papers (2020-01-17T19:52:54Z)
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.