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The Quasiparticle Random Phase Approximation equations are solved taking into account the Pauli Principle at the expectation value level, and allowing changes in the mean field occupation numbers to minimize the energy while having the…

Nuclear Theory · Physics 2009-11-06 Alejandro Mariano , Jorge G. Hirsch

In quantum algorithms discovered so far for simulating scattering processes in quantum field theories, state preparation is the slowest step. We present a new algorithm for preparing particle states to use in simulation of Fermionic Quantum…

Quantum Physics · Physics 2019-10-25 Ali Hamed Moosavian , Stephen Jordan

Sparse Principal Component Analysis (SPCA) is a fundamental technique for dimensionality reduction, and is NP-hard. In this paper, we introduce a randomized approximation algorithm for SPCA, which is based on the basic SDP relaxation. Our…

Machine Learning · Statistics 2026-05-19 Alberto Del Pia , Dekun Zhou

Particle-number projection within the Lipkin-Nogami (LN) method is applied to the self-consistent quasiparticle random-phase approximation (SCQRPA), which is tested in an exactly solvable multi-level pairing model. The SCQRPA equations are…

Nuclear Theory · Physics 2014-11-18 Nguyen Quang Hung , Nguyen Dinh Dang

We developed a method for computing matrix elements of single-particle operators in the correlated random phase approximation ground state. Working with the explicit random phase approximation ground state wavefunction, we derived…

Chemical Physics · Physics 2017-02-09 Daniel S. Kosov

In this work, we extend the Piris natural orbital functionals (PNOFs) to excited states by coupling their reconstructed second-order reduced density matrices with the extended random-phase approximation (ERPA). We have named the general…

High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on…

Numerical Analysis · Mathematics 2023-07-10 Jan S. Hesthaven , Cecilia Pagliantini , Nicolò Ripamonti

We develop Random Batch Methods for interacting particle systems with large number of particles. These methods use small but random batches for particle interactions, thus the computational cost is reduced from $O(N^2)$ per time step to…

Numerical Analysis · Mathematics 2019-09-25 Shi Jin , Lei Li , Jian-Guo Liu

We check the accuracy of the constrained random phase approximation (cRPA) downfolding scheme by considering one-dimensional two- and three-orbital Hubbard models with a target band at the Fermi level and one or two screening bands away…

Strongly Correlated Electrons · Physics 2018-12-31 Carsten Honerkamp , Hiroshi Shinaoka , Fakher F. Assaad , Philipp Werner

The real-space density-functional perturbation theory (DFPT) for the computations of the response properties with respect to the atomic displacement and homogeneous electric field perturbation has been recently developed and implemented…

Computational Physics · Physics 2020-10-28 Honghui Shang , Wanzhen Liang , Yunquan Zhang , Jinlong Yang

Localized molecular orbitals are often used for the analysis of chemical bonds, but they can also serve to efficiently and comprehensibly compute linear response properties. While conventional canonical molecular orbitals provide an…

Chemical Physics · Physics 2023-02-08 Souloke Sen , Bruno Senjean , Lucas Visscher

We present a general approach to converge excited state solutions to any quantum chemistry orbital optimization process, without the risk of variational collapse. The resulting Square Gradient Minimization (SGM) approach only requires…

Chemical Physics · Physics 2020-03-11 Diptarka Hait , Martin Head-Gordon

We present a new algorithm which is named the Dynamical Functional Particle Method, DFPM. It is based on the idea of formulating a finite dimensional damped dynamical system whose stationary points are the solution to the original…

Numerical Analysis · Mathematics 2013-03-25 Mårten Gulliksson , Sverker Edvardsson , Andreas Lind

Solving linear systems of equations is a frequently encountered problem in machine learning and optimisation. Given a matrix $A$ and a vector $\mathbf b$ the task is to find the vector $\mathbf x$ such that $A \mathbf x = \mathbf b$. We…

Quantum Physics · Physics 2018-02-07 Leonard Wossnig , Zhikuan Zhao , Anupam Prakash

The recent extensions of the covariant energy density functional theory with the quasiparticle-vibration coupling (QVC) are reviewed. Formulation of the Quasiparticle Random Phase Approximation (QRPA) in the relativistic framework is…

Nuclear Theory · Physics 2017-08-23 Elena Litvinova , Victor Tselyaev

The Exact Regularized Point Particle (ERPP) method is extended to treat the interphase momentum coupling between particles and fluid in the presence of walls by accounting for the vorticity generation due to the particles close to solid…

We give a rigorous and self-contained analysis of the Grover--Rudolph quantum state-preparation algorithm, which encodes a probability distribution $\{p_k\}$ as an $n$-qubit amplitude state $\sum_k\sqrt{p_k}\ket{k}$ via a hierarchy of…

Quantum Physics · Physics 2026-05-26 Antonio Falco , Daniela Falco-Pomares , Hermann G. Matthies

Configuration-interaction-type calculations on electronic and vibrational structure are often the method of choice for the reliable approximation of many-particle wave functions and energies. The exponential scaling, however, limits their…

Computational Physics · Physics 2019-05-24 Alberto Baiardi , Christopher J. Stein , Vincenzo Barone , Markus Reiher

Charged particle tracking has played a key role in the development of particle physics, particularly for understanding phenomena involving short-lived particles precisely. As a platform for high-resolution charged particle tracking, an…

Atomic Physics · Physics 2023-11-30 Akio Kawasaki

We design an algorithm for computing the $p$-curvature of a differential system in positive characteristic $p$. For a system of dimension $r$ with coefficients of degree at most $d$, its complexity is $\softO (p d r^\omega)$ operations in…

Symbolic Computation · Computer Science 2015-06-19 Alin Bostan , Xavier Caruso , Éric Schost