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 $\Lambda$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 $b \in L^1([0,1],\textit{BV}([0,1]^2))$ which are ergodic/weakly mixing/strongly mixing: this means that their Regular Lagrangian Flow $X_t$ is an ergodic/weakly mixing/strongly mixing measure preserving map when evaluated at $t=1$. Our main result is that there exists a $G_\delta$-set $\mathcal U \subset L^1_{t,x}([0,1]^3)$ made of divergence-free vector fields such that $1)$ the map $\Phi$ associating $b$ with its RLF $X_t$ can be extended as a continuous function to the $G_\delta$-set $\mathcal{U}$; $2)$ ergodic vector fields $b$ are a residual $G_\delta$-set in $\mathcal{U}$; $3)$ weakly mixing vector fields $b$ are a residual $G_\delta$-set in $\mathcal{U}$; $4)$ strongly mixing vector fields $b$ are a first category set in $\mathcal{U}$; $5)$ exponentially (fast) mixing vector fields are a dense subset of $\mathcal{U}$. The proof of these results is based on the density of BV vector fields such that $X_{t=1}$ 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 $d \geq 3$ 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)