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In truncated coupled-cluster (CC) theories, non-variational and/or generally complex ground-state energies can occur. This is due to the non-Hermitian nature of the similarity transformed Hamiltonian matrix in combination with CC…

Chemical Physics · Physics 2023-09-22 Tanner Culpitt , Erik I. Tellgren , Fabijan Pavosevic

The recent quantum information boom has effected a resurgence of interest in unitary coupled cluster (UCC) theory. Our group's interest in local energy landscapes of unitary ans\"atze prompted us to investigate the classical approach of…

Chemical Physics · Physics 2022-11-01 Harper R. Grimsley , Nicholas J. Mayhall

We present an analytical proof and numerical demonstrations of the equivalence of the correlation energy from particle-particle random phase approximation (pp-RPA) and ladder-couple-cluster-doubles (ladder-CCD). These two theories reduce to…

Chemical Physics · Physics 2013-11-01 Degao Peng , Stephan N. Steinmann , Helen van Aggelen , Weitao Yang

Stepped-wedge cluster randomised trials (SW-CRTs) increasingly evaluate complex interventions, yet methodological guidance for analysing composite endpoints using generalized pairwise comparisons (GPC)remains limited. This work investigates…

The classical Canonical Correlation Analysis (CCA) identifies the correlations between two sets of multivariate variables based on their covariance, which has been widely applied in diverse fields such as computer vision, natural language…

Optimization and Control · Mathematics 2024-01-02 Yongchun Li , Santanu S. Dey , Weijun Xie

We propose to use wavefunction overlaps obtained from a quantum computer as inputs for the classical split-amplitude techniques, tailored and externally corrected coupled cluster, to achieve balanced treatment of static and dynamic…

A full coupled-cluster expansion suitable for sparse algebraic operations is developed by expanding the commutators of the Baker-Campbell-Hausdorff series explicitly for cluster operators in binary representations. A full coupled-cluster…

Chemical Physics · Physics 2018-09-13 Enhua Xu , Motoyuki Uejima , Seiichiro L. Ten-no

The structure of oxide-supported metal nanoclusters plays an essential role in their sharply enhanced catalytic activity over bulk metals. Simulations provide the atomic-scale resolution needed to understand these systems. However, the…

Materials Science · Physics 2024-05-22 Benjamin X. Shi , David J. Wales , Angelos Michaelides , Chang Woo Myung

The coupled cluster method (CCM) is a method of quantum many-body theory that may provide accurate results for the ground-state properties of lattice quantum spin systems even in the presence of strong frustration and for lattices of…

Strongly Correlated Electrons · Physics 2009-11-11 D. J. J. Farnell , J. Schulenburg , J. Richter , K. A. Gernoth

Modern high-dimensional methods often adopt the "bet on sparsity" principle, while in supervised multivariate learning statisticians may face "dense" problems with a large number of nonzero coefficients. This paper proposes a novel…

Machine Learning · Statistics 2022-02-10 Yiyuan She , Jiahui Shen , Chao Zhang

We review the theory of weakly coupled oscillators for smooth systems. We then examine situations where application of the standard theory falls short and illustrate how it can be extended. Specific examples are given to non-smooth systems…

Adaptation and Self-Organizing Systems · Physics 2020-06-14 Bard Ermentrout , Youngmin Park , Dan Wilson

We report a complete implementation of the coupled-cluster method with single, double, and triple excitations (CCSDT) where tensor decompositions are used to reduce its scaling and overall computational costs. For the decomposition of the…

Chemical Physics · Physics 2020-02-20 Michał Lesiuk

A wide class of coupled-cluster methods is introduced, based on Arponen's extended coupled-cluster theory. This class of methods is formulated in terms of a coordinate transformation of the cluster operators. The mathematical framework for…

Chemical Physics · Physics 2020-12-02 Simen Kvaal , Andre Laestadius , Tilmann Bodenstein

An accurate determination of singlet-triplet gaps in biradicals, including cyclobutadiene in the automerization barrier region where one has to balance the substantial nondynamical many-electron correlation effects characterizing the…

Chemical Physics · Physics 2025-12-23 Swati S. Priyadarsini , Karthik Gururangan , Jun Shen , Piotr Piecuch

Deep clustering can optimize representations of instances (i.e., representation learning) and explore the inherent data distribution (i.e., clustering) simultaneously, which demonstrates a superior performance over conventional clustering…

Computer Vision and Pattern Recognition · Computer Science 2023-11-27 Qi Qian

Convex clustering has recently garnered increasing interest due to its attractive theoretical and computational properties, but its merits become limited in the face of high-dimensional data. In such settings, pairwise affinity terms that…

Methodology · Statistics 2021-04-02 Saptarshi Chakraborty , Jason Xu

Constrained clustering integrates domain knowledge through pairwise constraints. However, existing deep constrained clustering (DCC) methods are either limited by anchors inherent in end-to-end modeling or struggle with learning…

Machine Learning · Computer Science 2025-10-09 Shaojie Zhang , Ke Chen

In this paper, numerical simulations of four-mode continuous-variable cluster states with different topologies in the framework of measurement-based quantum computation are presented. By utilizing the symplectic representation and…

Quantum Physics · Physics 2026-04-28 Amin Ahadi , Saman Sarshar

In this paper, we have developed a unitary variant of a double exponential coupled cluster theory, which is capable of mimicking the effects of connected excitations of arbitrarily high rank, using only rank-one and rank-two parametrization…

Chemical Physics · Physics 2022-11-23 Dipanjali Halder , V. S. Prasannaa , Rahul Maitra

Sparse Principal Component Analysis (sPCA) is a cardinal technique for obtaining combinations of features, or principal components (PCs), that explain the variance of high-dimensional datasets in an interpretable manner. This involves…

Optimization and Control · Mathematics 2025-12-02 Ryan Cory-Wright , Jean Pauphilet
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