Machine Learning techniques applied to the statistical properties of spin systems

In recent years Machine Learning has proved to be successful in many technological applications and scientific tasks, such as image and speech recognition, natural language understanding and more. In this work we apply both supervised and unsupervised Machine Learning to three key problems in statistical physics: design of Hamiltonian from data, phase recognition and study of critical properties of a system undergoing phase transition. As an example of model design, we use a dataset with spin configurations and corresponding energies randomly sampled from a one dimensional Ising model. We then make two guesses for the correct Hamiltonian: the former involving interactions of spins with an external local field, the latter involving two body interactions among spins. The coupling constants are determined with a linear penalized regression, comparing the effects of L1 and L2 penalization terms of the cost function. We pay specific attention to the problem of overfitting and to the validation process, which is critical for accepting or rejecting the proposed model. The phase recognition problem is faced with the two dimensional Ising and XY models as examples. After showing the limits of a simple softmax regression for this task, we build suitable neural networks to overcome these limits. In particular, a feed forward network is built and the learning process is investigated for the Ising model; while a more sophisticated convolutional network is proposed for the XY model in order to detect local topological structures. The last part of the work is dedicated to the unsupervised study of phase transitions, and the determination of critical properties (order parameter, critical temperature, critical exponents). The discussed techniques are Principal Component Analysis (PCA) for dimensional reduction and K-means clustering for organizing data into subsets with specific properties. PCA is applied to the two dimensional Ising, Potts and XY models, and it is used to find suitable order parameters. The study of the proposed order parameter as a function of temperature provides evidence of phase transition, and a finite size scaling allows to extrapolate both the critical temperature in the thermodynamic limit, and the $\nu$ critical exponent for the correlation length. K-means clustering is applied to equilibrium configurations of the two dimensional Ising model, before and after the dimensional reduction, and the critical temperature is estimated. Moreover, a clustering of relaxation curves of magnetization in a Monte Carlo dynamics is used to build a phase diagram on the parameter space of temperature and magnetic field. A synergy of C++, Python and Wolfram Mathematica 12.0 is used throughout this work in order to sample input datasets and to build and control customized neural networks and learning tools. The most relevant codes are provided in the appendix.ope

Termodinamica di bosoni confinati e interagenti

The Bose-Einstein condensation (BEC) is a quantum phenomenon which was theorized in 1920s, but it was realized for the first time in the JILA Laboratory (University of Colorado, Boulder) by Eric Cornell and Carl Wieman only in 1995. Since 1995, lots of experiments on BEC have been realized using different gases and various conditions. One can give a correct interpretation of the results of these works only by considering the role of interaction between particles on relevant physical quantities, such as critical temperature and condensed fraction. The purpose of our work is deducing the main thermodynamic properties of trapped and interacting Bose gases using the tools of "traditional" Quantum Mechanics, avoiding the introduction of "second quantization" (which often recurs in litterature). The first chapter shows a derivation of the famous Gross-Pitaevskii equation, starting from the many-bodies Dirac action and applying the principle of least action. Moreover, two approximate solutions of this equation are discussed as well: the gaussian variational ansatz for weakly interacting particles, and the Thomas-Fermi approximation, for strongly interacting particles. In chapter 2 we derive the Bogoliubov dispersion relation for excited states (or quasiparticle energy spectrum) with the tools of Hamiltonian Mechanics and semiclassical approximation. Furthermore, we discuss some approximations for this formula (especially the Hartree-Fock spectrum for weakly interacting bosons) and we use the Bose-Einstein distribution to find the thermal cloud density. In chapter 3 we use the previous results to calculate explicit expressions for critical temperature and condensed fraction (where possible) in four particular cases: ideal free and trapped Bose gas, interacting free and trapped Bose gas. We also discuss the main results obtained by Giorgini, Pitaevskii and Stringari, who managed to explain accurately Cornell and Wieman's experimental plots. Finally we conclude that traditional Quantum Mechanics is quite efficient to derive the main equations of BEC and that introducing second quantization is not essential. Nevertheless, some issues remain unresolved in this context: for example the phenomenon of quantum depletion and the generalisation of the Gross-Pitaevskii equation at non-zero temperature.ope

Interaction-resistant metals in multicomponent Fermi systems

We analyze two different fermionic systems that defy Mott localization showing a metallic ground state at integer filling and very large Coulomb repulsion. The first is a multiorbital Hubbard model with a Hund's coupling, where this physics has been widely studied and the new metallic state is called a Hund's metal, and the second is a SU(3) Hubbard model with a patterned single-particle potential designed to display a similar interaction-resistant metal in a set-up which can be implemented with SU($N$) ultracold atoms. With simple analytical arguments and exact numerical diagonalization of the Hamiltonians for a minimal three-site system, we demonstrate that the interaction-resistant metal emerges in both cases as a compromise between two different insulating solutions which are stabilized by different terms of the models. This provides a strong evidence that the Hund's metal is a specific realization of a more general phenomenon which can be realized in various strongly correlated systems.Comment: 11 pages, 11 figure

Enhancement of chiral edge currents in (dd+1)-dimensional atomic Mott-band hybrid insulators

We consider the effect of a local interatomic repulsion on synthetic heterostructures where a discrete synthetic dimension is created by Raman processes on top of $SU(N)$-symmetric two-dimensional lattice systems. At a filling of one fermion per site, increasing the interaction strength, the system is driven towards a Mott state which is adiabatically connected to a band insulator. The chiral currents associated with the synthetic magnetic field increase all the way to the Mott transition, where they reach the maximum value, and they remain finite in the whole insulating state. The transition towards the Mott-band insulator is associated with the opening of a gap within the low-energy quasiparticle peak, while a mean-field picture is recovered deep in the insulating state.Comment: 27 Pages, 10 Figure

Machine Learning techniques applied to the statistical properties of spin systems

In recent years Machine Learning has proved to be successful in many technological applications and scientific tasks, such as image and speech recognition, natural language understanding and more. In this work we apply both supervised and unsupervised Machine Learning to three key problems in statistical physics: design of Hamiltonian from data, phase recognition and study of critical properties of a system undergoing phase transition. As an example of model design, we use a dataset with spin configurations and corresponding energies randomly sampled from a one dimensional Ising model. We then make two guesses for the correct Hamiltonian: the former involving interactions of spins with an external local field, the latter involving two body interactions among spins. The coupling constants are determined with a linear penalized regression, comparing the effects of L1 and L2 penalization terms of the cost function. We pay specific attention to the problem of overfitting and to the validation process, which is critical for accepting or rejecting the proposed model. The phase recognition problem is faced with the two dimensional Ising and XY models as examples. After showing the limits of a simple softmax regression for this task, we build suitable neural networks to overcome these limits. In particular, a feed forward network is built and the learning process is investigated for the Ising model; while a more sophisticated convolutional network is proposed for the XY model in order to detect local topological structures. The last part of the work is dedicated to the unsupervised study of phase transitions, and the determination of critical properties (order parameter, critical temperature, critical exponents). The discussed techniques are Principal Component Analysis (PCA) for dimensional reduction and K-means clustering for organizing data into subsets with specific properties. PCA is applied to the two dimensional Ising, Potts and XY models, and it is used to find suitable order parameters. The study of the proposed order parameter as a function of temperature provides evidence of phase transition, and a finite size scaling allows to extrapolate both the critical temperature in the thermodynamic limit, and the $\nu$ critical exponent for the correlation length. K-means clustering is applied to equilibrium configurations of the two dimensional Ising model, before and after the dimensional reduction, and the critical temperature is estimated. Moreover, a clustering of relaxation curves of magnetization in a Monte Carlo dynamics is used to build a phase diagram on the parameter space of temperature and magnetic field. A synergy of C++, Python and Wolfram Mathematica 12.0 is used throughout this work in order to sample input datasets and to build and control customized neural networks and learning tools. The most relevant codes are provided in the appendix

Effects of long-range hopping in the Bose-Hubbard model

We investigate the effects of an extended Bose-Hubbard model with a long-range hopping term on the Mott insulator-superfluid quantum phase transition. We consider the effects of a power-law decaying hopping term, and we show that the Mott phase is shrunk in the parameters' space. We provide an exact solution for one-dimensional lattices and also two reliable approximations for higher dimensions. Finally, we extend these results to a more realistic situation in which long-range hopping is made by a combination of power-law and screening terms, studying the main effects on the Mott lobes