Condensed Matter

Quantum localization (single-body or many-body) comes with the emergence of local conserved quantities -- whose conservation is precisely at the heart of the absence of transport through the system. In the case of fermionic systems and S=1/2 spin models, such conserved quantities take the form of effective two-level systems, called l -bits. While their existence is the defining feature of localized phases, their direct experimental observation remains elusive. Here we show that strongly localized l -bits bear a dramatic universal signature, accessible to state-of-the-art quantum simulators, in the form of periodic cusp singularities in the Loschmidt echo following a quantum quench from a Néel/charge-density-wave state. Such singularities are perfectly captured by a simple model of Rabi oscillations of an ensemble of independent two-level systems, which also reproduces the short-time behavior of the entanglement entropy and the imbalance dynamics. In the case of interacting localized phases, the dynamics at longer times shows a sharp crossover to a faster decay of the Loschmidt echo singularities, offering an experimentally accessible signature of the interactions between l -bits.

In this work we report on a loss of ergodicity in a simple hopping model, motivated by the Hubbard Hamiltonian, of a many body quantum system at zero temperature, quantized in Euclidean time. We show that this quantum system may lose ergodicity at high densities on a large lattice, as a result of both Pauli exclusion and strong Coulomb repulsion. In particular we study particle hopping susceptibilities and the tendency towards particle localization. It is found that the appearance and existence of quantum phase transitions in this model, in the case of high density and strong Coulomb repulsion, depends on the starting configuration of particle trajectories in the numerical simulation. This is presumably the Euclidean time version of a breakdown of the eigenstate thermalization hypothesis in real time quantization.

We studied the spectral dynamics of single fluorescent dye molecules embedded in ultrathin films (5 - 100 nm) of the amorphous polymer polyisobutylene at cryogenic temperatures and its variation with film thickness. Noticeable portion of molecules in the ensemble show a behavior which is inconsistent with the standard tunneling model: Their spectral lines are subject to irreversible spectral jumps, continuous shifting, and abrupt chaotic changes of the linewidth or jumping rate. In films thinner than 100 nm, the occurrence of "non-standard" spectral behavior increases with decreasing sample thickness at fixed excitation intensity. In addition, it also increases with laser intensity.

Atomic vibrations in perfect, slightly defective or mixed crystals are to a large extent well understood since many decades. Theoretical descriptions are thus in excellent agreement with the experiments. As a consequence, phonon-related properties like specific heat, thermal conductivity or sound attenuation are also well explained in these solids. This is not yet the case in glasses where the lack of periodicity generates enormous difficulties in theoretical treatments as well as in experiments or in numerical simulations. Thanks to recent developments along all these lines, comprehensive studies have emerged in the last decades and several decisive advances have been made. This chapter is thus devoted to a discussion of the nature of the vibrational properties in glasses with particular emphasis on the low-frequency part of the vibrational density of states, including the acoustic excitations, and of the experimental techniques used to their study.

We study a recently introduced and exactly solvable mean-field model for the density of vibrational states D(?) of a structurally disordered system. The model is formulated as a collection of disordered anharmonic oscillators, with random stiffness κ drawn from a distribution p(κ) , subjected to a constant field h and interacting bilinearly with a coupling of strength J . We investigate the vibrational properties of its ground state at zero temperature. When p(κ) is gapped, the emergent D(?) is also gapped, for small J . Upon increasing J , the gap vanishes on a critical line in the (h,J) phase diagram, whereupon replica symmetry is broken. At small h , the form of this pseudogap is quadratic, D(?)??? 2 , and its modes are delocalized, as expected from previously investigated mean-field spin glass models. However, we determine that for large enough h , a quartic pseudogap D(?)??? 4 , populated by localized modes, emerges, the two regimes being separated by a special point on the critical line. We thus uncover that mean-field disordered systems can generically display both a quadratic-delocalized and a quartic-localized spectrum at the glass transition.

Recent advances in machine learning have become increasingly popular in the applications of phase transitions and critical phenomena. By machine learning approaches, we try to identify the physical characteristics in the two-dimensional percolation model. To achieve this, we adopt Monte Carlo simulation to generate dataset at first, and then we employ several approaches to analyze the dataset. Four kinds of convolutional neural networks (CNNs), one variational autoencoder (VAE), one convolutional VAE (cVAE), one principal component analysis (PCA), and one k -means are used for identifying order parameter, the permeability, and the critical transition point. The former three kinds of CNNs can simulate the two order parameters and the permeability with high accuracy, and good extrapolating performance. The former two kinds of CNNs have high anti-noise ability. To validate the robustness of the former three kinds of CNNs, we also use the VAE and the cVAE to generate new percolating configurations to add perturbations into the raw configurations. We find that there is no difference by using the raw or the perturbed configurations to identify the physical characteristics, under the prerequisite of corresponding labels. In the case of lacking labels, we use unsupervised learning to detect the physical characteristics. The PCA, a classical unsupervised learning, performs well when identifying the permeability but fails to deduce order parameter. Hence, we apply the fourth kinds of CNNs with different preset thresholds, and identify a new order parameter and the critical transition point. Our findings indicate that the effectiveness of machine learning still needs to be evaluated in the applications of phase transitions and critical phenomena.

Known for their ability to identify hidden patterns in data, artificial neural networks are among the most powerful machine learning tools. Most notably, neural networks have played a central role in identifying states of matter and phase transitions across condensed matter physics. To date, most studies have focused on systems where different phases of matter and their phase transitions are known, and thus the performance of neural networks is well controlled. While neural networks present an exciting new tool to detect new phases of matter, here we demonstrate that when the training sets are poisoned (i.e., poor training data or mislabeled data) it is easy for neural networks to make misleading predictions.

We study the phase transitions of three-dimensional (3D) classical O(3) model and the two-dimensional (2D) classical XY model, as well as both the quantum phase transitions of 2D and 3D dimerized spin-1/2 antiferromagnets, using the techniques of supervised neural network (NN). Moreover, unlike the conventional approaches commonly used in the literature, the training sets employed in our investigation are neither the theoretical nor the real configurations of the considered systems. Remarkably, with such an unconventional set up of the training stage in conjunction with semi-experimental finite-size scaling formulas, the associated critical points determined by the NN method agree well with the established results in the literature. The outcomes obtained here imply that certain unconventional training strategies, like the one used in this study, are not only cost-effective in computation, but are also applicable for a wild range of physical systems.

Topological materials discovery has emerged as an important frontier in condensed matter physics. Recent theoretical approaches based on symmetry indicators and topological quantum chemistry have been used to identify thousands of candidate topological materials, yet experimental determination of materials' topology often poses significant technical challenges. X-ray absorption spectroscopy (XAS) is a widely-used materials characterization technique sensitive to atoms' local symmetry and chemical environment; thus, it may encode signatures of materials' topology, though indirectly. In this work, we show that XAS can potentially uncover materials' topology when augmented by machine learning. By labelling computed X-ray absorption near-edge structure (XANES) spectra of over 16,000 inorganic materials with their topological class, we establish a machine learning-based classifier of topology with XANES spectral inputs. Our classifier correctly predicts 81% of topological and 80% of trivial cases, and can achieve 90% and higher accuracy for materials containing certain elements. Given the simplicity of the XAS setup and its compatibility with multimodal sample environments, the proposed machine learning-empowered XAS topological indicator has the potential to discover broader categories of topological materials, such as non-cleavable compounds and amorphous materials. It can also inform a variety of field-driven phenomena in situ, such as magnetic field-driven topological phase transitions.

Using the multilayer convolutional neural network (CNN), we can detect the quantum phases in random electron systems, and phase diagrams of two and higher dimensional Anderson transitions and quantum percolations as well as disordered topological systems have been obtained. Here, instead of using CNN to analyze the wave functions, we analyze the dynamics of wave packets via long short-term memory network (LSTM). We adopt the quasi-periodic quantum kicked rotors, which simulate the three and four dimensional Anderson transitions. By supervised training, we let LSTM extract the features of the time series of wave packet displacements in localized and delocalized phases. We then simulate the wave packets in unknown phases and let LSTM classify the time series to localized and delocalized phases. We compare the phase diagrams obtained by LSTM and those obtained by CNN.