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Representing real-time data as a sum of complex exponentials provides a compact form that enables both denoising and extrapolation. As a fully data-driven method, the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is agnostic to the underlying physical equations, making it broadly applicable to various observables and experimental or numerical setups. In this work, we consider applications of the ESPRIT algorithm primarily to extend real-time dynamical data from simulations of quantum systems. We evaluate ESPRIT's performance in the presence of noise and compare it to other extrapolation methods. We demonstrate its ability to extract information from short-time dynamics to reliably predict long-time behavior and determine the minimum time interval required for accurate results. We discuss how this insight can be leveraged in numerical methods that propagate quantum systems in time, and show how ESPRIT can predict infinite-time values of dynamical observables, offering a purely data-driven approach to characterizing quantum phases. 

Quantum transport is often characterized not just by mean observables like the particle or energy current but by their fluctuations and higher moments, which can act as detailed probes of the physical mechanisms at play. However, relatively few theoretical methods are able to access the full counting statistics (FCS) of transport processes through electronic junctions in strongly correlated regimes. While most experiments are concerned with steady state properties, most accurate theoretical methods rely on computationally expensive propagation from a tractable initial state. Here, we propose a simple approach for computing the FCS through a junction directly at the steady state, utilizing the propagator noncrossing approximation. Compared to time propagation, our method offers reduced computational cost at the same level of approximation, but the idea can also be used within other approximations or as a basis for numerically exact techniques. We demonstrate the method’s capabilities by investigating the impact of lead dimensionality on electronic transport in the nonequilibrium Anderson impurity model at the onset of Kondo physics. Our results reveal a distinct signature of one dimensional leads in the noise and Fano factor not present for other dimensionalities, showing the potential of FCS measurements as a probe of the environment surrounding a quantum dot. 

Material properties of Ga–Sb binary alloy thin films deposited under ultra-high vacuum conditions were studied for analog phase change memory (PCM) applications. Crystallization of this alloy was shown to occur in the temperature range of 180–264 °C, with activation energy >2.5 eV depending on the composition. X-ray diffraction (XRD) studies showed phase separation upon crystallization into two phases, Ga-doped A7 antimony and cubic zinc-blende GaSb. Synchrotron in situ XRD analysis revealed that crystallization into the A7 phase is accompanied by Ga out-diffusion from the grains. X-ray absorption fine structure studies of the local structure of these alloys demonstrated a bond length decrease with a stable coordination number of 4 upon amorphous-to-crystalline phase transformation. Mushroom cell structures built with Ga–Sb alloys on ø110 nm TiN heater show a phase change material resistance switching behavior with resistance ratio >100 under electrical pulse measurements. TEM and Energy Dispersive Spectroscopy (EDS) studies of the Ga–Sb cells after ∼100 switching cycles revealed that partial SET or intermediate resistance states are attained by the variation of the grain size of the material as well as the Ga content in the A7 phase. A mechanism for a reversible composition control is proposed for analog cell performance. These results indicate that Te-free Ga–Sb binary alloys are potential candidates for analog PCM applications.