Related papers: Variational approach for pair optimization in the …
We study the nontrivial interplay of the well known \emph{pairing} and the more complex \emph{quarteting} correlations in the particular case of $N>Z$ atomic nuclei. Within the new Analytical Disentangled Condensate model, by implementing…
The overview of the Exact Pairing technique based on the quasispin symmetry is presented. Extensions of this method are discussed in relation to mean field, quadrupole collectivity, electromagnetic transitions, and many-body level density.…
We present a novel approach for adaptive, differentiable parameterization of large-scale random fields. If the approach is coupled with any gradient-based optimization algorithm, it can be applied to a variety of optimization problems,…
The pairing properties of the neutrinoless double-$\beta$ decay candidate $^{116}$Cd have been investigated. Measurements of the two-neutron removal reactions on isotopes of $^{114,116}$Cd have been made in order to identify 0$^+$ strength…
The phase transition of the one-dimensional, diffusive pair contact process (PCPD) is investigated by N cluster mean-field approximations and high precision simulations. The N=3,4 cluster approximations exhibit smooth transition line to…
Approximate inference in high-dimensional, discrete probabilistic models is a central problem in computational statistics and machine learning. This paper describes discrete particle variational inference (DPVI), a new approach that…
The lowest order constrained variational method is applied to calculate the polarized symmetrical nuclear matter properties with the modern $AV_{18}$ potential performing microscopic calculations. Results based on the consideration of…
Tumor cell populations can be thought of as being composed of homogeneous cell subpopulations, with each subpopulation being characterized by overlapping sets of single nucleotide variants (SNVs). Such subpopulations are known as subclones…
Probabilistic principal component analysis (PCA) and its Bayesian variant (BPCA) are widely used for dimension reduction in machine learning and statistics. The main advantage of probabilistic PCA over the traditional formulation is…
We develop techniques to convexify a set that is invariant under permutation and/or change of sign of variables and discuss applications of these results. First, we convexify the intersection of the unit ball of a permutation and…
We present an extension of the pair coupled cluster doubles (p-CCD) method to quasiparticles and apply it to the attractive pairing Hamiltonian. Near the transition point where number symmetry gets spontaneously broken, the proposed…
Typical path integral Monte Carlo approaches use the primitive approximation to compute the probability density for a given path. In this work, we develop the pair Discrete Variable Representation (pair-DVR) approach to study molecular…
Variational analysis techniques in lattice QCD are powerful tools that give access to the full spectrum of QCD. At zero momentum, these techniques are well established and can cleanly isolate energy eigenstates of either positive or…
First principles calculations of the form factors of baryon excitations are now becoming accessible through advances in Lattice QCD techniques. In this paper, we explore the utility of the parity-expanded variational analysis (PEVA)…
The recently-introduced Parity Expanded Variational Analysis (PEVA) technique allows for the isolation of baryon eigenstates on the lattice at finite momentum free from opposite-parity contamination. We find that this technique introduces a…
Quadrupole excitations of neutron-rich nuclei are analyzed by using the linear response method in the Quasiparticle Random Phase Approximation (QRPA). The QRPA response is derived starting from the time-dependent Hartree-Fock-Bogoliubov…
The estimation of directed couplings between the nodes of a network from indirect measurements is a central methodological challenge in scientific fields such as neuroscience, systems biology and economics. Unfortunately, the problem is…
We develop the variational/parquet diagram approach to the structure of nuclear systems with strongly state-dependent interactions. For that purpose, we combine ideas of the general Jastrow-Feenberg variational method and the local…
We present a new straightforward principal component analysis (PCA) method based on the diagonalization of the weighted variance-covariance matrix through two spectral decomposition methods: power iteration and Rayleigh quotient iteration.…
Principal component analysis (PCA) is a dimensionality reduction method in data analysis that involves diagonalizing the covariance matrix of the dataset. Recently, quantum algorithms have been formulated for PCA based on diagonalizing a…