Variational solutions are given for problems involving diverse fermionic and bosonic N=2--7-body systems. The trial wave functions are chosen to be combinations of correlated Gaussians, which facilitate a fully analytical calculation of the matrix elements. The nonlinear parameters of the trial function are selected by a stochastic technique. The method has proved efficient, accurate, and seems feasible for any few-body bound-state problems emerging in atomic or nuclear physics. \textcopyright{} 1996 The American Physical Society.
Variational solutions are given for problems involving diverse fermionic and bosonic N=2--7-body systems. The trial wave functions are chosen to be combinations of correlated Gaussians, which facilitate a fully analytical calculation of the matrix elements. The nonlinear parameters of the trial function are selected by a stochastic technique. The method has proved efficient, accurate, and seems feasible for any few-body bound-state problems emerging in atomic or nuclear physics. \textcopyright{} 1996 The American Physical Society.
Precise variational solutions are given for problems involving diverse fermionic and bosonic (N=2--7)-body systems. The trial wave functions are chosen to be combinations of correlated Gaussians, which are constructed from products of the single-particle Gaussian wave packets through an integral transformation, thereby facilitating fully analytical calculations of the matrix elements. The nonlinear parameters of the trial function are chosen by a stochastic technique. The method has proved very efficient, virtually exact, and it seems feasible for any few-body bound-state problems emerging in nuclear or atomic physics.
Quantum few-body systems are deceptively simple. Indeed, with the notable exception of a few special cases, their associated Schrodinger equation cannot be solved analytically for more than two particles. One has to resort to approximation methods to tackle quantum few-body problems. In particular, variational methods have been proposed to ease numerical calculations and obtain precise solutions. One such method is the Stochastic Variational Method, which employs a stochastic search to determine the number and parameters of correlated Gaussian basis functions used to construct an ansatz of the wave function. Stochastic methods, however, face numerical and optimization challenges as the number of particles increases. We introduce a family of gradient variational methods that replace stochastic search with gradient optimization. We comparatively and empirically evaluate the performance of the baseline Stochastic Variational Method, several instances of the gradient variational method family, and some hybrid methods for selected few-body problems. We show that gradient and hybrid methods can be more efficient and effective than the Stochastic Variational Method. We discuss the role of singularities, oscillations, and gradient optimization strategies in the performance of the respective methods.
Abstract Quantum few-body systems are deceptively simple. Indeed, with the notable exception of a few special cases, their associated Schrödinger equation cannot be solved analytically for more than two particles. One has to resort to approximation methods to tackle quantum few-body problems. In particular, variational methods have been proposed to ease numerical calculations and obtain precise solutions. One such method is the Stochastic Variational Method, which employs a stochastic search to determine the number and parameters of correlated Gaussian basis functions used to construct an ansatz of the wave function. Stochastic methods, however, face numerical and optimization challenges as the number of particles increases. We introduce a family of gradient variational methods that replace stochastic search with gradient optimization. We comparatively and empirically evaluate the performance of the baseline Stochastic Variational Method, several instances of the gradient variational method family, and some hybrid methods for selected few-body problems. We show that gradient and hybrid methods can be more efficient and effective than the Stochastic Variational Method. We discuss the role of singularities, oscillations, and gradient optimization strategies in the performance of the respective methods.
The description of Fermi systems based on Correlated Basis Functions (CBF) is a natural extension of the variational approach, in which the trial ground state wave function is written in the form. The main problem associated with the neglect, or the approximate treatment, of elementary diagrams is the possible violation of the variational principle, which may become significant at high density. Besides the capability to systematically improve upon the variational estimates of the ground-state energy, the CBF formalism provides a consistent framework to carry out calculations of different nuclear matter properties. In CBF perturbation theory, one has to evaluate matrix elements of the bare nuclear Hamiltonian, the effects of correlations being taken into account by the transformation of the basis states describing the non interacting system. However, the same result can in principle be obtained transforming the Hamiltonian, and using the Fermi gas basis.
Few-body correlations often express the distinguishing characteristic features of a many-body system. This thesis studies such correlations within dilute Bose-Einstein condensates in the case of arbitrary negative s-wave scattering length. The N-boson problem is solved by using an ab-initio approach based on correlated Gaussians that allows explicit inclusion of few-body correlations with a computational complexity that is independent of the number of particles. Calculations introducing all higher-order correlations are also done for small systems. In the weakly interacting regime, two-body correlations are not only the simplest but also the most important. By varying the scattering length and comparing the ground state energy for different explicitly correlated trial wave functions this assumption is investigated under both weakly and strongly interacting conditions.
Many variational descriptions of quantum many-body systems rest on an expansion over basis functions, and their practical limit is often set by the number of basis functions required. We propose the Bayesian variational method (BVM), in which the basis functions are selected by Bayesian optimization: a Gaussian-process surrogate model, conditioned on the candidates evaluated so far, predicts which candidates are most likely to lower the energy, and the candidate evaluations, being mutually independent, are distributed over many nodes. Two further ingredients make the method practical. An incremental diagonalization evaluates each candidate by reusing the previous diagonalization of the accepted basis instead of solving the full eigenvalue problem anew. A trimming procedure continually removes basis functions that have become nearly linearly dependent, keeping the accepted basis small while guiding it toward the optimal solution. The BVM applies broadly to energy variational problems based on basis-function expansions in quantum mechanics; here we apply it to the Gaussian expansion method (GEM), a standard approach in few-body physics. Because the GEM basis is nonorthogonal, its linear dependence is strong, so the basis reduction achieved by the BVM is large. The reduction both accelerates the computation and, more importantly, greatly reduces the memory requirement, one of the central bottlenecks of the variational method: the reference energy of the full 32,000-dimensional GEM diagonalization is reproduced to within 0.01 K with only 705 basis functions and to within 0.001 K with 2,127, corresponding to memory reductions of 99.95% and 99.56%, since the matrix storage grows as the square of the basis dimension. Within the GEM, this opens a path to the precision study of six- and seven-body systems, and beyond, that has so far been difficult to reach.