arXiv subjects

Juana Moreno

Publications and source records attributed to Juana Moreno.

At least 19 recordsLinked to original sources

arXiv2025

Interplay of non-Hermitian skin effect and electronic correlations in the non-Hermitian Hubbard model via Real-space dynamical mean field theory

Non-Hermitian quantum systems, characterized by their ability to model open systems with gain and loss, have unveiled striking phenomena such as the non-Hermitian skin effect (NHSE), where eigenstates localize at boundaries under open boundary conditions. While extensively studied in non-interacting systems, the interplay between NHSE and strong electron correlations remains largely unexplored. Here, we investigate the non-Hermitian Hubbard model with asymmetric hopping, employing real-space dynamical mean-field theory (R-DMFT), a novel extension to such non-Hermitian correlated models-to capture both local correlations and spatial inhomogeneities. By analyzing the end-to-end Green's function as probes of directional amplification, we demonstrate that strong correlations can suppress the skin effect, leading to a crossover from boundary-dominated to correlation-driven dynamics. Our systematic study reveals that correlations dominate at small to intermediate strength of asymmetric hopping, inducing exponential decay in the end-to-end Green's function, but higher strength of asymmetric hopping can restore amplification even at strong interaction. These results illuminate the tunable interplay between correlations and non-Hermitian physics, suggesting avenues for engineering non-reciprocal transport in correlated open quantum systems.

cond-mat.str-el↗

arXiv2024

Disorder Enhanced Thermalization in Interacting Many-Particle System

We introduce an extension of the non-equilibrium dynamical mean field theory to incorporate the effects of static random disorder in the dynamics of a many-particle system by integrating out different disorder configurations resulting in an effective time-dependent density-density interaction. We use this method to study the non-equilibrium transient dynamics of a system described by the Fermi Anderson-Hubbard model following an interaction and disorder quench. The method recovers the solution of the disorder-free case for which the system exhibits qualitatively distinct dynamical behaviors in the weak-coupling (prethermalization) and strong-coupling regimes (collapse-and-revival oscillations). However, we find that weak random disorder promotes thermalization. In the weak coupling regime, the jump in the quasiparticle weight in the prethermal regime is suppressed by random disorder while in the strong-coupling regime, random disorder reduces the amplitude of the quasiparticle weight oscillations. These results highlight the importance of disorder in the dynamics of realistic many-particle systems.

cond-mat.str-el↗

arXiv2024

Out of Time Order Correlation of the Hubbard Model with Random Local Disorder

The out of time order correlator (OTOC) serves as a powerful tool for investigating quantum information spreading and chaos in complex systems. We present a method employing non-equilibrium dynamical mean-field theory (DMFT) and coherent potential approximation (CPA) combined with diagrammatic perturbation on the Schwinger-Keldysh contour to calculate the OTOC for correlated fermionic systems subjected to both random disorder and electrons interaction. Our key finding is that random disorder enhances the OTOC decay in the Hubbard model for the metallic phase in the weak coupling limit. However, the current limitation of our perturbative solver restricts the applicability to weak interaction regimes.

cond-mat.str-el↗

arXiv2023

Quantum Classical Algorithm for the Study of Phase Transitions in the Hubbard Model via Dynamical Mean-Field Theory

Simulating quantum many-body systems is believed to be one of the most promising applications of near-term noisy quantum computers. However, in the near term, system size limitation will remain a severe barrier for applications in materials science or strongly correlated systems. A promising avenue of research is to combine many-body physics with machine learning for the classification of distinct phases. In this paper, we propose a workflow that synergizes quantum computing, many-body theory, and quantum machine learning(QML) for studying strongly correlated systems. In particular, it can capture a putative quantum phase transition of the stereotypical strongly correlated system, the Hubbard model. Following the recent proposal of the hybrid classical-quantum algorithm for the two-site dynamical mean-field theory(DMFT), we present a modification that allows the self-consistent solution of the single bath site DMFT. The modified algorithm can easily be generalized for multiple bath sites. This approach is used to generate a database of zero-temperature wavefunctions of the Hubbard model within the DMFT approximation. We then use a QML algorithm to distinguish between the metallic phase and the Mott insulator phase to capture the metal-to-Mott insulator phase transition. We train a quantum convolutional neural network(QCNN) and then utilize the QCNN as a quantum classifier to capture the phase transition region. This work provides a recipe for application to other phase transitions in strongly correlated systems and represents an exciting application of small-scale quantum devices realizable with near-term technology.

cond-mat.str-el↗

arXiv2023

Engineering non-Hermitian Second Order Topological Insulator in Quasicrystals

Non-Hermitian topological phases have gained immense attention due to their potential to unlock novel features beyond Hermitian bounds. PT-symmetric (Parity Time-reversal symmetric) non-Hermitian models have been studied extensively over the past decade. In recent years, the topological properties of general non-Hermitian models, regardless of the balance between gains and losses, have also attracted vast attention. Here we propose a non-Hermitian second-order topological (SOT) insulator that hosts gapless corner states on a two-dimensional quasi-crystalline lattice (QL). We first construct a non-Hermitian extension of the Bernevig-Hughes-Zhang (BHZ) model on a QL generated by the Amman-Beenker (AB) tiling. This model has real spectra and supports helical edge states. Corner states emerge by adding a proper Wilson mass term that gaps out the edge states. We propose two variations of the mass term that result in fascinating characteristics. In the first variation, we obtain a purely real spectra for the second-order topological phase. In the latter, we get a complex spectra with corner states localized at only two corners. Our findings pave a path to engineering exotic SOT phases where corner states can be localized at designated corners.

cond-mat.mes-hall↗

arXiv2022

Nonequilibrium DMFT+CPA for Correlated Disordered Systems

We present a solution for the nonequilibrium dynamics of an interacting disordered system. The approach adapts the combination of the equilibrium dynamical mean field theory (DMFT) and the equilibrium coherent potential approximation (CPA) methods to the nonequilibrium many-body formalism, using the Kadanoff-Baym-Keldysh complex time contour, for the dynamics of interacting disordered systems away from equilibrium. We use our time domain solution to obtain the equilibrium density of states of the disordered interacting system described by the Anderson-Hubbard model, bypassing the necessity for the cumbersome analytical continuation process. We further apply the nonequilibrium solution to the interaction quench problem for an isolated disordered system. Here, the interaction is abruptly changed from zero (non-interacting system) to another constant (finite) value at which it is subsequently kept. We observe via the time-dependence of the potential, kinetic, and total energies, the effect of disorder on the relaxation of the system as a function of final interaction strength. The real-time approach has the potential to shed new light on the fundamental role of disorder in the nonequilibrium dynamics of interacting quantum systems.

cond-mat.str-el↗

arXiv2022

Beyond Quantum Cluster Theories: Multiscale Approaches for Strongly Correlated Systems

The degrees of freedom that confer to strongly correlated systems their many intriguing properties also render them fairly intractable through typical perturbative treatments. For this reason, the mechanisms responsible for these technologically promising properties remain mostly elusive. Computational approaches have played a major role in efforts to fill this void. In particular, dynamical mean field theory (DMFT) and its cluster extension, the dynamical cluster approximation (DCA) have allowed significant progress. However, despite all the insightful results of these embedding schemes, computational constraints, such as the minus sign problem in Quantum Monte Carlo (QMC), and the exponential growth of the Hilbert space in exact diagonalization (ED) methods, still limit the length scale within which correlations can be treated exactly in the formalism. A recent advance to overcome these difficulties is the development of multiscale many body approaches whereby this challenge is addressed by introducing an intermediate length scale between the short length scale where correlations are treated exactly using a cluster solver such QMC or ED, and the long length scale where correlations are treated in a mean field manner. At this intermediate length scale correlations can be treated perturbatively. This is the essence of multiscale many-body methods. We will review various implementations of these multiscale many-body approaches, the results they have produced, and the outstanding challenges that should be addressed for further advances.

cond-mat.str-el↗

arXiv2021

An Application of Quantum Machine Learning on Quantum Correlated Systems: Quantum Convolutional Neural Network as a Classifier for Many-Body Wavefunctions from the Quantum Variational Eigensolver

Machine learning has been applied on a wide variety of models, from classical statistical mechanics to quantum strongly correlated systems for the identification of phase transitions. The recently proposed quantum convolutional neural network (QCNN) provides a new framework for using quantum circuits instead of classical neural networks as the backbone of classification methods. We present here the results from training the QCNN by the wavefunctions of the variational quantum eigensolver for the one-dimensional transverse field Ising model (TFIM). We demonstrate that the QCNN identifies wavefunctions which correspond to the paramagnetic phase and the ferromagnetic phase of the TFIM with good accuracy. The QCNN can be trained to predict the corresponding phase of wavefunctions around the putative quantum critical point, even though it is trained by wavefunctions far away from it. This provides a basis for exploiting the QCNN to identify the quantum critical point.

cond-mat.str-el↗

arXiv2021

Application of the Variational Autoencoder to Detect the Critical Points of the Anisotropic Ising Model

We generalize the previous study on the application of variational autoencoders to the two-dimensional Ising model to a system with anisotropy. Due to the self-duality property of the system, the critical points can be located exactly for the entire range of anisotropic coupling. This presents an excellent testbed for the validity of using a variational autoencoder to characterize an anisotropic classical model. We reproduce the phase diagram for a wide range of anisotropic couplings and temperatures via a variational autoencoder without the explicit construction of an order parameter. Considering that the partition function of $(d+1)$-dimensional anisotropic models can be mapped to that of the $d$-dimensional quantum spin models, the present study provides numerical evidence that a variational autoencoder can be applied to analyze quantum systems via Quantum Monte Carlo.

cond-mat.dis-nn↗

arXiv2021

Real Space Quantum Cluster Formulation for the Typical Medium Theory of Anderson Localization

We develop a real space cluster extension of the typical medium theory (cluster-TMT) to study Anderson localization. By construction, the cluster-TMT approach is formally equivalent to the real space cluster extension of the dynamical mean field theory. Applying the developed method to the 3D Anderson model with a box disorder distribution, we demonstrate that cluster-TMT successfully captures the localization phenomena in all disorder regimes. As a function of the cluster size, our method obtains the correct critical disorder strength for the Anderson localization in 3D, and systematically recovers the re-entrance behavior of the mobility edge. From a general perspective, our developed methodology offers the potential to study Anderson localization at surfaces within quantum embedding theory.

cond-mat.dis-nn↗

arXiv2020

Influence of State Reopening Policies in COVID-19 Mortality

By the end of May 2020, all states in the US have eased their COVID-19 mitigation measures. Different states adopted markedly different policies and timing for reopening. An important question remains in how the relaxation of mitigation measures is related to the number of casualties. To address this question, we compare the actual data to a hypothetical case in which the mitigation measures are left intact using a projection of the data from before mitigation measures were eased. We find that different states have shown significant differences in the number of deaths, possibly due to their different policies and reopening schedules. Our study provides a gauge for the effectiveness of the approaches by different state governments and can serve as a guide for implementing best policies in the future. It also indicates that the face mask mandate seems to correlate with the change in the death count more dramatically than other measures.

physics.soc-ph↗

arXiv2020

Effect of Mitigation Measures on the Spreading of COVID-19 in Hard-Hit States

State government-mandated social distancing measures have helped to slow down the growth of the COVID-19 pandemic in the United States. Current predictive models of the development of COVID-19, especially after mitigation efforts, are largely based on extrapolating the data from other countries. Since most states enforced stay-at-home orders towards the end of March, their effect should be reflected in the death and infection counts at the end of April. Using the data available until April 25th, we investigate the change in the infection rate due to the mitigation efforts, and project death and infection counts until September, 2020, for some of the most heavily impacted states: New York, New Jersey, Michigan, Massachusetts, Illinois and Louisiana. We find that with the current mitigation efforts five of those six states reduce their reproduction number to a value less than one, stopping the exponential growth of the pandemic. We also projected different scenarios after the mitigation is relaxed. Analysis for other states can be found at https://covid19projection.org/.

q-bio.PE↗

arXiv2020

Projected Development of COVID-19 in Louisiana

At the time of writing, Louisiana has the third highest COVID-19 infection per capita in the United States. The state government issued a stay-at-home order effective March 23rd. We analyze the projected spread of COVID-19 in Louisiana without including the effects of the stay-at-home order. We predict that a large fraction of the state population would be infected without the mitigation efforts, and would certainly overwhelm the capacity of Louisiana health care system. We further predict the outcomes with different degrees of reduction in the infection rate. More than 70% of reduction is required to cap the number of infected to under one million.

q-bio.PE↗

arXiv2019

Introduction to Computational Thinking: a new high school curriculum using CodeWorld

The Louisiana Department of Education partnered with the Gordon A. Cain Center at LSU to pilot a Computing High School Graduation Pathway. The first course in the pathway, Introduction to Computational Thinking (ICT), is designed to teach programming and reinforce mathematical practice skills of nine-grade students, with an emphasis on promoting higher order thinking. In 2017-18, about 200 students and five teachers participated in the pilot, in 2018-2019 the participation increased to 400 students, and in the current 2019-2020 year about 800 students in 11 schools are involved. Professional development starts with a five-week intensive summer institute, which is complemented with follow-up Saturday sessions and coaching support during the academic year. After describing the course content and briefly the teacher training, we discuss the data we have collected in the last two years. The overall student reception of the course has been positive, but the course was categorized by most students as hard. However, the Computing Attitude Survey analysis indicates that the difficulty of the course did not demotivate the students. The pre-post test content assessments show that students learned not only the language, but also general principles of programming, logic and modeling, as well as use of variables, expressions and functions. Lessons learned during the pilot phase motivated changes, such as emphasizing during PD the need to provide timely feedback to students, provide detailed rubrics for the projects and reorganize the lessons to increase the initial engagement with the material. After two years of running pilots, the course is becoming student-centered, where most of the code and image samples provided in the lessons are based on code created by previous students.

cs.CY↗

arXiv2019

Interplay of strong correlations and covalency in ionic band insulators

We address the role of electronic correlations in different kinds of band insulators by using the two-orbital Hubbard model within the dynamical mean-field theory (DMFT). An intriguing finding is that electronic correlations turn a metal into a band insulator when ionicity and covalency are equal in ratio.We conclude that the electronic correlations favour metallicity when the covalency is smaller than the ionicity, while they favour insulating behaviour when the covalency is greater than ionicity.

cond-mat.str-el↗

arXiv2019

Localization of phonons in mass disordered alloys - A typical medium dynamical cluster approach

The effect of disorder on lattice vibrational modes has been a topic of interest for several decades. In this work, we employ a Green's function based approach, namely the dynamical cluster approximation (DCA), to investigate phonons in mass disordered systems. Detailed benchmarks with previous exact calculations are used to validate the method in a wide parameter space. An extension of the method, namely the typical medium DCA (TMDCA), is used to study Anderson localization of phonons in three dimensions. We show that, for binary isotopic disorder, lighter impurities induce localized modes beyond the bandwidth of the host system, while heavier impurities lead to a partial localization of the low-frequency acoustic modes. For a uniform (box) distribution of masses, the physical spectrum is shown to develop long tails comprising mostly localized modes. The mobility edge separating extended and localized modes, obtained through the TMDCA, agrees well with results from the transfer matrix method. A re-entrance behavior of the mobility edge with increasing disorder is found that is similar to, but somewhat more pronounced than, the behavior in disordered electronic systems. Our work establishes a new computational approach, which recovers the thermodynamic limit, is versatile and computationally inexpensive, to investigate lattice vibrations in disordered lattice systems.

cond-mat.dis-nn↗

arXiv2019

Periodic Anderson model with Holstein phonons for the description of the Cerium volume collapse

Recent experiments have suggested that the electron-phonon coupling may play an important role in the $γ\rightarrow α$ volume collapse transition in Cerium. A minimal model for the description of such transition is the periodic Anderson model. In order to better understand the effect of the electron-phonon interaction on the volume collapse transition, we study the periodic Anderson model with coupling between Holstein phonons and electrons in the conduction band. We find that the electron-phonon coupling enhances the volume collapse, which is consistent with experiments in Cerium. While we start with the Kondo Volume Collapse scenario in mind, our results capture some interesting features of the Mott scenario, such as a gap in the conduction electron spectra which grows with the effective electron-phonon coupling.

cond-mat.str-el↗

arXiv2016

A local theory for Mott-Anderson localization

The paramagnetic metallic phase of the Anderson-Hubbard model (AHM) is investigated using a non-perturbative local moment approach within the framework of dynamical mean field theory with a typical medium. Our focus is on the breakdown of the metallic phase near the metal-insulators transition as seen in the single-particle spectra, scattering rates and the associated distribution of Kondo scales. We demonstrate the emergence of a universal, underlying low energy scale, $T_K^{peak}$. This lies close to the peak of the distribution of Kondo scales obtained within the metallic phase of the paramagnetic AHM. Spectral dynamics for energies, $ω\lesssim T_K^{peak}$ display Fermi liquid universality crossing over to an incoherent universal dynamics for $ω\gg T_K^{peak}$ in the scaling regime. Such universal dynamics indicate that within a local theory the low to moderately low energy physics is governed by an effective, {\it disorder renormalised} Kondo screening.

cond-mat.str-el↗