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We compute nuclear response functions by solving the time-dependent A-body Schr\"odinger equation, recording the time-dependent transition moment and extracting spectral information via Fourier transforms. The solution of the time-dependent…

We show here that the Hamiltonian for an electronic system may be written exactly in terms of fluctuation operators that transition constituent fragments between internally correlated states, accounting rigorously for inter-fragment…

Chemical Physics · Physics 2019-05-24 Anthony D. Dutoi , Yuhong Liu

An algorithm, based on numerical description of the terms of many-body perturbation theory (Goldstone diagrams), is presented. The algorithm allows the use of the same piece of computer code to evaluate any particular diagram in any…

Atomic Physics · Physics 2015-05-13 V. A. Dzuba

The simulation of strongly correlated electron systems remains a formidable challenge. Certain experimentally relevant dynamical response functions are especially difficult to calculate, due to issues of finite-size effects and the ill…

Strongly Correlated Electrons · Physics 2026-03-25 Petar Brinić , Hugo U. R. Strand , Jakša Vučičević

The quantum many-body bound-state problem in its computationally successful coupled cluster method (CCM) representation is reconsidered. In conventional practice one factorizes the ground-state wave functions $|\Psi\rangle= e^S…

Quantum Physics · Physics 2014-05-02 Raymond F. Bishop , Miloslav Znojil

The fermionic many-body problem in the strong correlation regime is notoriously difficult to tackle. In a previous work (Phys. Rev. B 101, 045109 (2020)), we have proposed to extend the single-reference coupled-cluster (SRCC) method to the…

Strongly Correlated Electrons · Physics 2024-11-13 Dominic Bergeron

Using many-body perturbation theory and coupled-cluster theory, we calculate the ground-state energy of He-4 and O-16. We perform these calculations using a no-core G-matrix interaction derived from a realistic nucleon-nucleon potential.…

Nuclear Theory · Physics 2009-11-10 D. J. Dean , M. Hjorth-Jensen

We study numerically the formation of entanglement clusters across the many-body localization phase transition. We observe a crossover from strong many-body entanglement in the ergodic phase to weak local correlations in the localized…

Disordered Systems and Neural Networks · Physics 2019-04-24 Loïc Herviou , Soumya Bera , Jens H. Bardarson

Shallow, CNOT-efficient quantum circuits are crucial for performing accurate computational chemistry simulations on current noisy quantum hardware. Here, we explore the usefulness of non-iterative energy corrections, based on the method of…

Chemical Physics · Physics 2023-05-03 Ilias Magoulas , Francesco A. Evangelista

We detail the application of bounding volume hierarchies to accelerate second-virial evaluations for arbitrary complex particles interacting through hard and soft finite-range potentials. This procedure, based on the construction of…

Soft Condensed Matter · Physics 2017-12-14 Maxime M. C. Tortora , Jonathan P. K. Doye

Introducing low-energy effective Hamiltonians is usual to grasp most correlations in quantum many-body problems. For instance, such effective Hamiltonians can be treated at the mean-field level to reproduce some physical properties of…

Quantum Gases · Physics 2025-01-09 Raphaël Photopoulos , Antoine Boulet

Topologically ordered systems are characterized by topological invariants that are often calculated from the momentum space integration of a certain function that represents the curvature of the many-body state. The curvature function may…

Mesoscale and Nanoscale Physics · Physics 2016-01-21 Wei Chen

We introduce a coarse-graining transformation for tensor networks that can be applied to study both the partition function of a classical statistical system and the Euclidean path integral of a quantum many-body system. The scheme is based…

Strongly Correlated Electrons · Physics 2015-11-04 Glen Evenbly , Guifre Vidal

We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Using hierarchical matrix approximations for the kernel matrix the memory requirements, the number of floating point operations, and the…

Machine Learning · Computer Science 2018-03-29 Elizaveta Rebrova , Gustavo Chavez , Yang Liu , Pieter Ghysels , Xiaoye Sherry Li

High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex and discontinuous nature of this problem pose significant…

Methodology · Statistics 2022-10-11 Rungang Han , Yuetian Luo , Miaoyan Wang , Anru R. Zhang

Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions. Within the complex brain system, differences in neuronal connection strengths parcellate…

Machine Learning · Computer Science 2023-05-09 Wei Dai , Hejie Cui , Xuan Kan , Ying Guo , Sanne van Rooij , Carl Yang

A general quantum many-body theory in configuration space is developed by extending the traditional coupled cluter method (CCM) to a variational formalism. Two independent sets of distribution functions are introduced to evaluate the…

Strongly Correlated Electrons · Physics 2009-11-07 Y. Xian

This paper proposes a novel higher-order multi-scale (HOMS) computational method, which is highly targeted for efficient, high-accuracy and low-computational-cost simulation of hygro-thermo-mechanical (H-T-M) coupling problems in…

Numerical Analysis · Mathematics 2025-12-11 Hao Dong , Yifei Ding , Jiale Linghu , Yufeng Nie , Yaochuang Han

A comprehensive and detailed account is presented for the finite-temperature many-body perturbation theory for electrons that expands in power series all thermodynamic functions on an equal footing. Algebraic recursions in the style of the…

Statistical Mechanics · Physics 2021-09-21 So Hirata

We present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism exploiting an artificial neural network representation of the ground-state wave function. A memory-efficient version of…

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