We have studied the magnetic, magnetotransport, and galvanomagnetic properties of ${\mathrm{TmNiC}}_2$. We find that the antiferromagnetic and field induced metamagnetic and ferromagnetic orderings do not suppress the charge density wave. The persistence of Fermi surface pockets, open as a result of imperfect nesting accompanying the Peierls transition, results in an electronic carriers mobility of the order of $4\times {10}^3{\mathrm{cm}}^2{\mathrm{V}}^{-1}{\mathrm{s}}^{-1}$ in ferromagnetic state, without any signatures for a significant deterioration of nesting properties. This is independently evidenced by high, nonsaturating linear magnetoresistance reaching 440% at $T=2\mathrm{K}$ and an analysis of the Hall conductivity. We thus demonstrate that, the coexistence of charge density wave and magnetism provides an alternative route to maintain high electronic mobility in the magnetically ordered state.

We reveal a dramatic departure of electron thermodiffusion in solids relative to the commonly accepted picture of the ideal free-electron gas model. In particular, we show that the interaction with the lattice and impurities, combined with a strong material dependence of the electron dispersion relation, leads to counterintuitive diffusion behavior, which we identify by comparing a two-dimensional electron gas (2DEG) and single-layer graphene. When subject to a temperature gradient $\nabla T$, thermodiffusion of massless Dirac fermions in graphene exhibits an anomalous behavior with electrons moving along $\nabla T$ and accumulating in hot regions, in contrast to normal electron diffusion in a 2DEG with parabolic dispersion, where net motion against $\nabla T$ is observed, accompanied by electron depletion in hot regions. These findings bear fundamental importance for the understanding of the spatial electron dynamics in emerging materials, establishing close relations with other branches of physics dealing with electron systems under nonuniform temperature conditions.

We investigate low-temperature electronic properties of the nondimeric organic superconductor ${\beta }^{\prime \prime }\text{−}(\mathrm{ET}{)}_4[({\mathrm{H}}_3\mathrm{O})\mathrm{Ga}({\mathrm{C}}_2{\mathrm{O}}_4{)}_3]\mathrm{Ph}{\mathrm{NO}}_2$. By examining ultrasonic properties, charge disproportionation (CD) without magnetic field dependence is detected below $T_{\mathrm{CD}}\sim 8\mathrm{K}$ just above the superconducting critical temperature $T_c\sim 6\mathrm{K}$. From quantum oscillations in high fields, we find variation in the Fermi surface and mass enhancement induced by the CD. Heat capacity studies elucidate that the superconducting gap function is fully gapped in the Fermi surface, but anisotropic with fourfold symmetry. We point out that the pairing mechanism of the superconductivity is possibly dominated by charge fluctuations.

Current-induced spin-orbit torques (SOTs) allow for the efficient electrical manipulation of magnetism in spintronic devices. Engineering the SOT efficiency is a key goal that is pursued by maximizing the active interfacial spin accumulation or modulating the nonequilibrium spin density that builds up through the spin Hall and inverse spin galvanic effects. Regardless of the origin, the fundamental requirement for the generation of the current-induced torques is a net spin accumulation. We report on the large enhancement of the SOT efficiency in thulium iron garnet (TmIG)/Pt by capping with a ${\mathrm{CuO}}_x$ layer. Considering the weak spin-orbit coupling (SOC) of ${\mathrm{CuO}}_x$, these surprising findings likely result from an orbital current generated at the interface between ${\mathrm{CuO}}_x$ and Pt, which is injected into the Pt layer and converted into a spin current by strong SOC. The converted spin current decays across the Pt layer and exerts a “nonlocal” torque on TmIG. This additional torque leads to a maximum colossal enhancement of the SOT efficiency of a factor 16 for 1.5 nm of Pt at room temperature, thus opening a path to increase torques while at the same time offering insights into the underlying physics of orbital transport, which has so far been elusive.

We report a magnetostriction study of a perovskite ${\text{LaCoO}}_3$ above 100 T using our state-of-the-art strain gauge to investigate an interplay between electron correlation and spin crossover. There has been a controversy regarding whether two novel phases in ${\text{LaCoO}}_3$ at high magnetic fields result from crystallizations or Bose-Einstein condensation during spin crossover as manifestations of localization and delocalization in spin states, respectively. We show that both phases are crystallizations rather than condensations, and the two crystallizations are different, based on the observations that the two phases exhibit as magnetostriction plateaux with distinct heights. The crystallizations of spin states have emerged manifesting the localizations and interactions in spin crossover with large and cooperative lattice changes.

Deep learning has achieved impressive prediction accuracies in a variety of scientific and industrial domains. However, the nested nonlinear feature of deep learning makes the learning highly nontransparent, i.e., it is still unknown how the learning coordinates a huge number of parameters to achieve decision-making. To explain this hierarchical credit assignment, we propose a mean-field learning model by assuming that an ensemble of subnetworks, rather than a single network, is trained for a classification task. Surprisingly, our model reveals that apart from some deterministic synaptic weights connecting two neurons at neighboring layers, there exists a large number of connections that can be absent, and other connections can allow for a broad distribution of their weight values. Therefore, synaptic connections can be classified into three categories: very important ones, unimportant ones, and those of variability that may partially encode nuisance factors. Therefore, our model learns the credit assignment leading to the decision and predicts an ensemble of sub-networks that can accomplish the same task, thereby providing insights toward understanding the macroscopic behavior of deep learning through the lens of distinct roles of synaptic weights.

We propose and test a method to interpolate sparsely sampled signals by a stochastic process with a broad range of spatial and/or temporal scales. To this end, we extend the notion of a fractional Brownian bridge, defined as fractional Brownian motion with a given scaling (Hurst) exponent $H$ and with prescribed start and end points, to a bridge process with an arbitrary number of intermediate and nonequidistant points. Determining the optimal value of the Hurst exponent $H_{\mathrm{opt}}$, appropriate to interpolate the sparse signal, is a very important step of our method. We demonstrate the validity of our method on a signal from fluid turbulence in a high Reynolds number flow and discuss the implications of the non-self-similar character of the signal. The method introduced here could be instrumental in several physical problems, including astrophysics, particle tracking, and specific tailoring of surrogate data, as well as in domains of natural and social sciences.

We demonstrate how to explore phase diagrams with automated and unsupervised machine learning to find regions of interest for possible new phases. In contrast to supervised learning, where data is classified using predetermined labels, we here perform anomaly detection, where the task is to differentiate a normal dataset, composed of one or several classes, from anomalous data. As a paradigmatic example, we explore the phase diagram of the extended Bose Hubbard model in one dimension at exact integer filling and employ deep neural networks to determine the entire phase diagram in a completely unsupervised and automated fashion. As input data for learning, we first use the entanglement spectra and central tensors derived from tensor-networks algorithms for ground-state computation and later we extend our method and use experimentally accessible data such as low-order correlation functions as inputs. Our method allows us to reveal a phase-separated region between supersolid and superfluid parts with unexpected properties, which appears in the system in addition to the standard superfluid, Mott insulator, Haldane-insulating, and density wave phases.

Ground state or relaxed inorganic structures are the starting point for most computational materials science or surface science analyses. Many of these structure relaxations represent systematic changes to the structure, but there are currently no general methods to improve the initial structure guess based on past calculations. Here we present a method to directly predict the ground state configuration using differentiable optimization and graph neural networks to learn the properties of a simple harmonic force field that approximates the ground state structure and properties. We demonstrate this flexible open source tool for improving the initial configurations for large datasets of inorganic multicomponent surface relaxations across 32 elements and the relaxation of adsorbates (H and CO) on these surfaces. Using these improved initial configurations reduces the expensive adsorbate-covered surface relaxations by approximately 50% and is complementary to other approaches to accelerate the relaxation process.

Multiorbital systems such as the iron-based superconductors provide a new avenue to attack the long-standing problem of superconductivity in strongly correlated systems. In this work we study the superconductivity driven by a generic bosonic mechanism in a multiorbital model including the full dynamical electronic correlations induced by the Hubbard $U$ and the Hund’s coupling. We show that superconductivity survives much more in a Hund’s metal than in an ordinary correlated metal with the same degree of correlation. The redistribution of spectral weight characteristic of the Hund’s metal reflects also in the enhancement of the orbital-selective character of the superconducting gaps, in agreement with experiments in iron-based superconductors.

Highly excited Rydberg states of excitons in ${\mathrm{Cu}}_2\mathrm{O}$ semiconductors provide a promising approach to explore and control strong particle interactions in a solid-state environment. A major obstacle has been the substantial absorption background that stems from exciton-phonon coupling and lies under the Rydberg excitation spectrum, weakening the effects of exciton interactions. Here, we demonstrate that two-photon excitation of Rydberg excitons under conditions of electromagnetically induced transparency (EIT) can be used to control this background. Based on a microscopic theory that describes the known single-photon absorption spectrum, we analyze the conditions under which two-photon EIT permits separating the optical Rydberg excitation from the phonon-induced absorption background, and even suppressing it entir7ely. Our findings thereby pave the way for the exploitation of Rydberg blockade with ${\mathrm{Cu}}_2\mathrm{O}$ excitons in nonlinear optics and other applications.

We observe high optical quality factor ($Q$) polygonal and star coherent optical modes in a lithium niobate microdisk. In contrast to the previous polygon modes achieved by deformed microcavities at lower mechanical and optical $Q$, we adopt weak perturbation from a tapered fiber for the polygon mode formation. The resulting high intracavity optical power of the polygon modes triggers second harmonic generation at high efficiency. With the combined advantages of a high mechanical $Q$ cavity, we observe optomechanical oscillation in polygon modes for the first time. Finally, we observe frequency microcomb generation from the polygon modes with an ultrastable taper-on-disk coupling mechanism.

Control over minivalley polarization and interlayer coupling is demonstrated in double bilayer graphene twisted with an angle of 2.37°. This intermediate angle is small enough for the minibands to form and large enough such that the charge carrier gases in the layers can be tuned independently. Using a dual-gated geometry we identify and control all possible combinations of minivalley polarization via the population of the two bilayers. An applied displacement field opens a band gap in either of the two bilayers, allowing us to even obtain full minivalley polarization. In addition, the carriers, formerly separated by their minivalley character, are mixed by tuning through a Lifshitz transition, where the Fermi surface topology changes. The high degree of control over the minivalley character of the bulk charge transport in twisted double bilayer graphene offers new opportunities for realizing valleytronics devices such as valley valves, filters, and logic gates.

Quantum compiling, a process that decomposes the quantum algorithm into a series of hardware-compatible commands or elementary gates, is of fundamental importance for quantum computing. We introduce an efficient algorithm based on deep reinforcement learning that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set. It generates near-optimal gate sequences with given accuracy and is generally applicable to various scenarios, independent of the hardware-feasible universal set and free from using ancillary qubits. For concreteness, we apply this algorithm to the case of topological compiling of Fibonacci anyons and obtain near-optimal braiding sequences for arbitrary single-qubit unitaries. Our algorithm may carry over to other challenging quantum discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum physics.

We present an efficient approach to achieving arbitrary, high-fidelity control of a multilevel quantum system using optimal control techniques. As an demonstration, we implement a continuous, software-defined microwave pulse to realize a $0\leftrightarrow 2$ SWAP gate that achieves an average gate fidelity of 99.4%. We describe our procedure for extracting the system Hamiltonian, calibrating the quantum and classical hardware chain, and evaluating the gate fidelity. Our work represents an alternative, fully generalizable route towards achieving universal quantum control by leveraging optimal control techniques.

Motivated by nonzero neutrino masses and the possibility of new physics discovery, a number of experiments search for neutrinoless double beta decay. While hunting for this hypothetical nuclear process, a significant amount of two-neutrino double beta decay data have become available. Although these events are regarded and studied mostly as the background of neutrinoless double beta decay, they can also be used to probe physics beyond the standard model. In this Letter, we show how the presence of right-handed leptonic currents would affect the energy distribution and angular correlation of the outgoing electrons in two-neutrino double beta decay. Consequently, we estimate constraints imposed by currently available data on the existence of right-handed neutrino interactions without having to assume their nature. In this way, our results complement the bounds coming from the nonobservation of neutrinoless double beta decay as they limit also the exotic interactions of Dirac neutrinos. We perform a detailed calculation of two-neutrino double beta decay under the presence of exotic (axial-) vector currents, and we demonstrate that current experimental searches can be competitive to existing limits.