Efficient Retrieval and Ranking of Undesired Package Cycles in Large Software Systems

International audienceMany design guidelines state that a software system architecture should avoid cycles between its packages. Yet such cycles appear again and again in many programs. We believe that the existing approaches for cycle detection are too coarse to assist the developers to remove cycles from their programs. In this paper, we describe an efficient algorithm that performs a fine-grained analysis of the cycles among the packages of an application. In addition, we define a metric to rank cycles by their level of undesirability, prioritizing the cycles that seems the more undesired by the developers. Our approach is validated on two large and mature software systems in Java and Smalltalk

Statistical Mechanics of Dictionary Learning

Finding a basis matrix (dictionary) by which objective signals are represented sparsely is of major relevance in various scientific and technological fields. We consider a problem to learn a dictionary from a set of training signals. We employ techniques of statistical mechanics of disordered systems to evaluate the size of the training set necessary to typically succeed in the dictionary learning. The results indicate that the necessary size is much smaller than previously estimated, which theoretically supports and/or encourages the use of dictionary learning in practical situations.Comment: 6 pages, 4 figure

Generalization Error in Deep Learning

Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this article, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results

Multiplexed dispersive readout of superconducting phase qubits

We introduce a frequency-multiplexed readout scheme for superconducting phase qubits. Using a quantum circuit with four phase qubits, we couple each qubit to a separate lumped-element superconducting readout resonator, with the readout resonators connected in parallel to a single measurement line. The readout resonators and control electronics are designed so that all four qubits can be read out simultaneously using frequency multiplexing on the one measurement line. This technology provides a highly efficient and compact means for reading out multiple qubits, a significant advantage for scaling up to larger numbers of qubits.Comment: 4 pages, 4 figure

Excitation of superconducting qubits from hot non-equilibrium quasiparticles

Superconducting qubits probe environmental defects such as non-equilibrium quasiparticles, an important source of decoherence. We show that "hot" non-equilibrium quasiparticles, with energies above the superconducting gap, affect qubits differently from quasiparticles at the gap, implying qubits can probe the dynamic quasiparticle energy distribution. For hot quasiparticles, we predict a non-neligable increase in the qubit excited state probability P_e. By injecting hot quasiparticles into a qubit, we experimentally measure an increase of P_e in semi-quantitative agreement with the model and rule out the typically assumed thermal distribution.Comment: Main paper: 5 pages, 5 figures. Supplement: 1 page, 1 figure, 1 table. Updated to user-prepared accepted version. Key changes: Supplement added, Introduction rewritten, Figs.2,3,5 revised, Fig.4 adde

Planar Superconducting Resonators with Internal Quality Factors above One Million

We describe the fabrication and measurement of microwave coplanar waveguide resonators with internal quality factors above 10 million at high microwave powers and over 1 million at low powers, with the best low power results approaching 2 million, corresponding to ~1 photon in the resonator. These quality factors are achieved by controllably producing very smooth and clean interfaces between the resonators' aluminum metallization and the underlying single crystal sapphire substrate. Additionally, we describe a method for analyzing the resonator microwave response, with which we can directly determine the internal quality factor and frequency of a resonator embedded in an imperfect measurement circuit.Comment: 4 pages, 3 figures, 1 tabl

Spectral signatures of many-body localization with interacting photons

Statistical mechanics is founded on the assumption that a system can reach thermal equilibrium, regardless of the starting state. Interactions between particles facilitate thermalization, but, can interacting systems always equilibrate regardless of parameter values\,? The energy spectrum of a system can answer this question and reveal the nature of the underlying phases. However, most experimental techniques only indirectly probe the many-body energy spectrum. Using a chain of nine superconducting qubits, we implement a novel technique for directly resolving the energy levels of interacting photons. We benchmark this method by capturing the intricate energy spectrum predicted for 2D electrons in a magnetic field, the Hofstadter butterfly. By increasing disorder, the spatial extent of energy eigenstates at the edge of the energy band shrink, suggesting the formation of a mobility edge. At strong disorder, the energy levels cease to repel one another and their statistics approaches a Poisson distribution - the hallmark of transition from the thermalized to the many-body localized phase. Our work introduces a new many-body spectroscopy technique to study quantum phases of matter