86 research outputs found
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
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