Realizing time crystals in discrete quantum few-body systems

The exotic phenomenon of time translation symmetry breaking under periodic driving - the time crystal - has been shown to occur in many-body systems even in clean setups where disorder is absent. In this work, we propose the realization of time-crystals in few-body systems, both in the context of trapped cold atoms with strong interactions and of a circuit of superconducting qubits. We show how these two models can be treated in a fairly similar way by adopting an effective spin chain description, to which we apply a simple driving protocol. We focus on the response of the magnetization in the presence of imperfect pulses and interactions, and show how the results can be interpreted, in the cold atomic case, in the context of experiments with trapped bosons and fermions. Furthermore, we provide a set of realistic parameters for the implementation of the superconducting circuit.Comment: 6 pages, 4 figure

In field N transfer, build-up, and leaching in ryegrass-clover mixtures

Two field experiments investigating dynamics in grass-clover mixtures were conducted, using 15N- and 14C-labelling to trace carbon (C) and nitrogen (N) from grass (Lolium perenne L.) and clover (Trifolium repens L. and Trifolium pratense L.). The leaching of dissolved inorganic nitrogen (DIN), as measured in pore water sampled by suction cups, increased during the autumn and winter, whereas the leaching of dissolved organic nitrogen (DON) was fairly constant during this period. Leaching of 15N from the sward indicated that ryegrass was the direct source of less than 1-2 percent of the total N leaching measured, whereas N dynamics pointed to clover as an important contributor to N leaching. Sampling of roots indicates that the dynamics in smaller roots were responsible for N and C build-up in the sward, and that N became available for transfer among species and leaching from the root zone. The bi-directional transfer of N between ryegrass and clover could however not be explained only by root turnover. Other processes like direct uptake of organic N compounds, may have contributed

The Evolution of Entrepreneurial Competencies: A Longitudinal Study of University Spin-Off Venture Emergence

This paper aims to better understand the development of entrepreneurial competencies to create new ventures within the non-commercial academic environment. We build upon the evolutionary perspective considering where resources come from to help define these competencies and explain their paths of development. The study follows the creation and early growth of four university spin-offs within the UK and Norway. We identified three competencies of opportunity refinement, leveraging, and championing that appeared crucial for the ventures to gain credibility. Although selected competencies were inherent within the academic founders, the specific competencies for venture creation had to be developed or acquired. This was achieved iteratively through entrepreneurial experience and accessing competencies from disparate actors such as industry partners and equity investors. Propositions are offered to guide future empirical research based upon our framework

Multiqubit State Learning with Entangling Quantum Generative Adversarial Networks

The increasing success of classical generative adversarial networks (GANs) has inspired several quantum versions of GANs. Fully quantum mechanical applications of such quantum GANs have been limited to one- and two-qubit systems. In this paper, we investigate the entangling quantum GAN (EQ-GAN) for multiqubit learning. We show that the EQ-GAN can learn a circuit more efficiently compared to a swap test. We also consider the EQ-GAN for learning VQE-approximated eigenstates, and find that it generates excellent overlap matrix elements when learning VQE states of small molecules. However, this does not directly translate to a good estimate of the energy due to a lack of phase estimation. Finally, we consider random state learning with the EQ-GAN for up to six qubits, using different two-qubit gates, and show that it is capable of learning completely random quantum states, something which could be useful in quantum state loading.Comment: 6 pages, 4 figures, 1 table + Supporting materia

Initial results from the Caltech/DRSI balloon-borne isotope experiment

The Caltech/DSRI balloonborne High Energy Isotope Spectrometer Telescope (HEIST) was flown successfully from Palestine, Texas on 14 May, 1984. The experiment was designed to measure cosmic ray isotopic abundances from neon through iron, with incident particle energies from approx. 1.5 to 2.2 GeV/nucleon depending on the element. During approximately 38 hours at float altitude, 100,000 events were recorded with Z or = 6 and incident energies approx. 1.5 GeV/nucleon. We present results from the ongoing data analysis associated with both the preflight Bevalac calibration and the flight data

Fortran coarray implementation of semi-lagrangian convected air particles within an atmospheric model

This work added semi-Lagrangian convected air particles to the Intermediate Complexity Atmospheric Research (ICAR) model. The ICAR model is a simplified atmospheric model using quasi-dynamical downscaling to gain performance over more traditional atmospheric models. The ICAR model uses Fortran coarrays to split the domain amongst images and handle the halo region communication of the image’s boundary regions. The newly implemented convected air particles use trilinear interpolation to compute initial properties from the Eulerian domain and calculate humidity and buoyancy forces as the model runs. This paper investigated the performance cost and scaling attributes of executing unsaturated and saturated air particles versus the original particle-less model. An in-depth analysis was done on the communication patterns and performance of the semi-Lagrangian air particles, as well as the performance cost of a variety of initial conditions such as wind speed and saturation mixing ratios. This study found that given a linear increase in the number of particles communicated, there is an initial decrease in performance, but that it then levels out, indicating that over the runtime of the model, there is an initial cost of particle communication, but that the computational benefits quickly offset it. The study provided insight into the number of processors required to amortize the additional computational cost of the air particles