5,581 research outputs found
Machine learning approach for quantum non-Markovian noise classification
In this paper, machine learning and artificial neural network models are
proposed for quantum noise classification in stochastic quantum dynamics. For
this purpose, we train and then validate support vector machine, multi-layer
perceptron and recurrent neural network, models with different complexity and
accuracy, to solve supervised binary classification problems. By exploiting the
quantum random walk formalism, we demonstrate the high efficacy of such tools
in classifying noisy quantum dynamics using data sets collected in a single
realisation of the quantum system evolution. In addition, we also show that for
a successful classification one just needs to measure, in a sequence of
discrete time instants, the probabilities that the analysed quantum system is
in one of the allowed positions or energy configurations, without any external
driving. Thus, neither measurements of quantum coherences nor sequences of
control pulses are required. Since in principle the training of the machine
learning models can be performed a-priori on synthetic data, our approach is
expected to find direct application in a vast number of experimental schemes
and also for the noise benchmarking of the already available noisy
intermediate-scale quantum devices.Comment: 14 pages, 3 figures, 3 table
Dark Radiation and Inflationary Freedom after Planck 2015
The simplest inflationary models predict a primordial power spectrum (PPS) of
the curvature fluctuations that can be described by a power-law function that
is nearly scale-invariant. It has been shown, however, that the low-multipole
spectrum of the CMB anisotropies may hint the presence of some features in the
shape of the scalar PPS, which could deviate from its canonical power-law form.
We study the possible degeneracies of this non-standard PPS with the neutrino
anisotropies, the neutrino masses, the effective number of relativistic species
and a sterile neutrino or a thermal axion mass. The limits on these additional
parameters are less constraining in a model with a non-standard PPS when only
including the temperature auto-correlation spectrum measurements in the data
analyses. The inclusion of the polarization spectra noticeably helps in
reducing the degeneracies, leading to results that typically show no deviation
from the CDM model with a standard power-law PPS.Comment: 22 pages, 17 figures, 11 tables. Updated to match the published
version. Text abridged upon the referee's request
Properties of Mixing BV vector fields
We consider the density properties of divergence-free vector fields which are ergodic/weakly mixing/strongly
mixing: this means that their Regular Lagrangian Flow is an
ergodic/weakly mixing/strongly mixing measure preserving map when evaluated at
.
Our main result is that there exists a -set made of divergence-free vector fields such that
the map associating with its RLF can be extended as a
continuous function to the -set ;
ergodic vector fields are a residual -set in
;
weakly mixing vector fields are a residual -set in
;
strongly mixing vector fields are a first category set in
;
exponentially (fast) mixing vector fields are a dense subset of
.
The proof of these results is based on the density of BV vector fields such
that is a permutation of subsquares, and suitable perturbations of
this flow to achieve the desired ergodic/mixing behavior. These approximation
results have an interest of their own.
A discussion on the extension of these results to is also
presented.Comment: 47 page
Noise fingerprints in quantum computers: Machine learning software tools
In this paper we present the high-level functionalities of a
quantum-classical machine learning software, whose purpose is to learn the main
features (the fingerprint) of quantum noise sources affecting a quantum device,
as a quantum computer. Specifically, the software architecture is designed to
classify successfully (more than 99% of accuracy) the noise fingerprints in
different quantum devices with similar technical specifications, or distinct
time-dependences of a noise fingerprint in single quantum machines.Comment: 9 pages, 2 figure
Observing relationships between lightning and cloud profiles by means of a satellite-borne cloud radar
Abstract. Cloud electrification and related lightning activity in thunderstorms have their origin in the charge separation and resulting distribution of charged iced particles within the cloud. So far, the ice distribution within convective clouds has been investigated mainly by means of ground-based meteorological radars. In this paper we show how the products from Cloud Profiling Radar (CPR) on board CloudSat, a polar satellite of NASA's Earth System Science Pathfinder (ESSP), can be used to obtain information from space on the vertical distribution of ice particles and ice content and relate them to the lightning activity. The analysis has been carried out, focusing on 12 convective events over Italy that crossed CloudSat overpasses during significant lightning activity. The CPR products considered here are the vertical profiles of cloud ice water content (IWC) and the effective radius (ER) of ice particles, which are compared with the number of strokes as measured by a ground lightning network (LINET). Results show a strong correlation between the number of strokes and the vertical distribution of ice particles as depicted by the 94 GHz CPR products: in particular, cloud upper and middle levels, high IWC content and relatively high ER seem to be favourable contributory causes for CG (cloud to ground) stroke occurrence
The role of data embedding in equivariant quantum convolutional neural networks
Geometric deep learning refers to the scenario in which the symmetries of a
dataset are used to constrain the parameter space of a neural network and thus,
improve their trainability and generalization. Recently this idea has been
incorporated into the field of quantum machine learning, which has given rise
to equivariant quantum neural networks (EQNNs). In this work, we investigate
the role of classical-to-quantum embedding on the performance of equivariant
quantum convolutional neural networks (EQCNNs) for the classification of
images. We discuss the connection between the data embedding method and the
resulting representation of a symmetry group and analyze how changing
representation affects the expressibility of an EQCNN. We numerically compare
the classification accuracy of EQCNNs with three different basis-permuted
amplitude embeddings to the one obtained from a non-equivariant quantum
convolutional neural network (QCNN). Our results show a clear dependence of
classification accuracy on the underlying embedding, especially for initial
training iterations. The improvement in classification accuracy of EQCNN over
non-equivariant QCNN may be present or absent depending on the particular
embedding and dataset used. It is expected that the results of this work can be
useful to the community for a better understanding of the importance of data
embedding choice in the context of geometric quantum machine learning.Comment: 12 pages, 9 figures. Significant changes compared to previous
version. New results adde
Classification of cancer pathology reports: a large-scale comparative study
We report about the application of state-of-the-art deep learning techniques
to the automatic and interpretable assignment of ICD-O3 topography and
morphology codes to free-text cancer reports. We present results on a large
dataset (more than 80 000 labeled and 1 500 000 unlabeled anonymized reports
written in Italian and collected from hospitals in Tuscany over more than a
decade) and with a large number of classes (134 morphological classes and 61
topographical classes). We compare alternative architectures in terms of
prediction accuracy and interpretability and show that our best model achieves
a multiclass accuracy of 90.3% on topography site assignment and 84.8% on
morphology type assignment. We found that in this context hierarchical models
are not better than flat models and that an element-wise maximum aggregator is
slightly better than attentive models on site classification. Moreover, the
maximum aggregator offers a way to interpret the classification process.Comment: 10 pages, 6 figures, 3 tables, accepted for publication in IEEE
Journal of Biomedical and Health Informatics (J-BHI
Correlates of calcaneal quantitative ultrasound parameters in patients with diabetes: the study on the assessment of determinants of muscle and bone strength abnormalities in diabetes
OBJECTIVE: Quantitative ultrasound (QUS) provides an estimate of bone mineral
density (BMD) and also evaluates bone quality, which has been related to
increased fracture risk in people with diabetes. This study aimed at assessing
the correlates of calcaneal QUS parameters in diabetic subjects encompassing
various degrees of micro and macrovascular complications and a wide-range of
peripheral nerve function.
METHODS: Four hundred consecutive diabetic patients were examined by QUS to
obtain values of broadband ultrasound attenuation (BUA), the speed of sound
(SOS), quantitative ultrasound index (QUI), and BMD.
RESULTS: Among surrogate measures of complications, sensory and motor nerve
amplitude and heart rate response to cough test and standing correlated with QUS
parameters at univariate analysis, together with age, body mass index (BMI),
waist circumference, lipid profile, and renal function. Multivariate analysis
revealed that BUA, SOS, QUI, and BMD were independently associated with age, male
gender, hemoglobin A1c, BMI (or fat, but not fat-free mass), and somatic and
autonomic nerve function parameters.
CONCLUSIONS: These data indicate that peripheral nerve dysfunction is associated
with worse QUS parameters, possibly contributing to increased fracture risk in
diabetes. The positive relation of QUS measures with adiposity needs further
investigation. This trial is registered with ClinicalTrials.gov (NCT01600924)
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