Directly imaging spin polarons in a kinetically frustrated Hubbard system

The emergence of quasiparticles in quantum many-body systems underlies the rich phenomenology in many strongly interacting materials. In the context of doped Mott insulators, magnetic polarons are quasiparticles that usually arise from an interplay between the kinetic energy of doped charge carriers and superexchange spin interactions. However, in kinetically frustrated lattices, itinerant spin polarons - bound states of a dopant and a spin-flip - have been theoretically predicted even in the absence of superexchange coupling. Despite their important role in the theory of kinetic magnetism, a microscopic observation of these polarons is lacking. Here we directly image itinerant spin polarons in a triangular lattice Hubbard system realised with ultracold atoms, revealing enhanced antiferromagnetic correlations in the local environment of a hole dopant. In contrast, around a charge dopant, we find ferromagnetic correlations, a manifestation of the elusive Nagaoka effect. We study the evolution of these correlations with interactions and doping, and use higher-order correlation functions to further elucidate the relative contributions of superexchange and kinetic mechanisms. The robustness of itinerant spin polarons at high temperature paves the way for exploring potential mechanisms for hole pairing and superconductivity in frustrated systems. Furthermore, our work provides microscopic insights into related phenomena in triangular lattice moir\'{e} materials.Comment: 7 pages (4 figures) + 6 pages methods (7 figures

Visualizing Strange Metallic Correlations in the 2D Fermi-Hubbard Model with AI

Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- and short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. Our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems.Comment: 12 pages, 9 figures; updated in accord with the published versio

Subdiffusion and heat transport in a tilted 2D Fermi-Hubbard system

Using quantum gas microscopy we study the late-time effective hydrodynamics of an isolated cold-atom Fermi-Hubbard system subject to an external linear potential (a "tilt"). The tilt is along one of the principal directions of the two-dimensional (2D) square lattice and couples mass transport to local heating through energy conservation. We study transport and thermalization in our system by observing the decay of prepared initial density waves as a function of wavelength $\lambda$ and tilt strength and find that the associated decay time $\tau$ crosses over as the tilt strength is increased from characteristically diffusive to subdiffusive with $\tau\propto\lambda^4$. In order to explain the underlying physics we develop a hydrodynamic model that exhibits this crossover. For strong tilts, the subdiffusive transport rate is set by a thermal diffusivity, which we are thus able to measure as a function of tilt in this regime. We further support our understanding by probing the local inverse temperature of the system at strong tilts, finding good agreement with our theoretical predictions. Finally, we discuss the relation of the strongly tilted limit of our system to recently studied 1D models which may exhibit nonergodic dynamics.Comment: 7 pages with 5 figures in main text, 5 pages with 3 figures in Supplemental Materia

Visualizing strange metallic correlations in the two-dimensional Fermi-Hubbard model with artificial intelligence

Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- A nd short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. Our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems

A two-dimensional programmable tweezer array of fermions

We prepare high-filling two-component arrays of up to fifty fermionic atoms in optical tweezers, with the atoms in the ground motional state of each tweezer. Using a stroboscopic technique, we configure the arrays in various two-dimensional geometries with negligible Floquet heating. Full spin- and density-resolved readout of individual sites allows us to post-select near-zero entropy initial states for fermionic quantum simulation. We prepare a correlated state in a two-by-two tunnel-coupled Hubbard plaquette, demonstrating all the building blocks for realizing a programmable fermionic quantum simulator

Quality of life, psychological morbidity and family stress in elderly residing in the community

Este estudo procurou investigar as relações existentes entre morbilidade psicológica, stress familiar e qualidade de vida (QV) da pessoa idosa. A amostra foi constituída por 126 idosos. Os instrumentos utilizados foram: The Lawton Instrumental Activities of Daily Living (IADL), Quality of Life (WHOQOL-Bref), Geriatric Anxiety Inventory (GSI), Geriatric Depression Scale (GDS); e Index of Family Relations (IFR). Os resultados revelaram a importância da idade, estado civil, escolaridade e número de patologias assim como o género na capacidade funcional, morbilidade, stress familiar e QV. Ao nível dos preditores, a depressão foi a variável que mais contribuiu para a QV. Não foram encontradas variáveis moderadoras no modelo. A discussão e implicações dos resultados são abordadas bem como a intervenção psicológica nesta população.This study sought to understand the relationships among psychological morbidity, family stress and quality of life (QL) of elderly. The sample consisted of 126 elderly. The following instruments were used: the Lawton Instrumental Activities of Daily Living (IADL); Quality of Life (WHOQOL-Bref), Geriatric Anxiety Inventory (GSI), Geriatric Depression Scale (GDS), and the Index of Family Relations (IFR). Results revealed the importance of age, marital status, education and number of pathologies as well as gender on functional capacity, morbidity, family stress and QV. In terms of predictors, depression was the variable that contributed the most to QL. There were no moderating variables in the model. Discussion and implications of results are addressed as well as psychological interventions.(undefined