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We demonstrate the implementation of a novel machine learning framework for probability density estimation and classification using quantum circuits. The framework maps a training data set or a single data sample to the quantum state of a physical system through quantum feature maps. The quantum state of the arbitrarily large training data set summarises its probability distribution in a finite-dimensional quantum wave function. By projecting the quantum state of a new data sample onto the quantum state of the training data set, one can derive statistics to classify or estimate the density of the new data sample. Remarkably, the implementation of our framework on a real quantum device does not require any optimisation of quantum circuit parameters. Nonetheless, we discuss a variational quantum circuit approach that could leverage quantum advantage for our framework.

A density matrix describes the statistical state of a quantum system. It is a powerful formalism to represent both the quantum and classical uncertainty of quantum systems and to express different statistical operations such as measurement, system combination and expectations as linear algebra operations. This paper explores how density matrices can be used as a building block for machine learning models exploiting their ability to straightforwardly combine linear algebra and probability. One of the main results of the paper is to show that density matrices coupled with random Fourier features could approximate arbitrary probability distributions over Rn. Based on this finding the paper builds different models for density estimation, classification and regression. These models are differentiable, so it is possible to integrate them with other differentiable components, such as deep learning architectures and to learn their parameters using gradient-based optimization. In addition, the paper presents optimization-less training strategies based on estimation and model averaging. The models are evaluated in benchmark tasks and the results are reported and discussed.

This paper presents a multimodal latent topic analysis method for the construction of image collection summaries. The method automatically selects a set of prototypical images from a large set of retrieved images for a given query. We define an image collection summary as a subset of images from a collection, which is visually and semantically representative. To build such a summary we propose MICS (Multimodal Image Collection Summarization), a method that combines textual and visual modalities in a common latent space, which allows to find a subset of images from which the whole collection can be reconstructed. Experiments were conducted on two collections of tagged images demonstrating the ability of the approach to build summaries with representative visual and semantic contents. The method was evaluated using objective measures, reconstruction error and diversity of the summary, showing competitive results when compared to other summarization approaches. [ABSTRACT FROM AUTHOR]

A model that can detect anomalies, even when trained only with normal samples, and can learn from encounters with new anomalies is proposed. The model combines a negative selection algorithm and a self-organizing map (SOM) in an immune inspired architecture. One of the main advantages of the proposed system is that it is able to produce a visual representation of the self/non-self feature space, thanks to the topological two-dimensional map produced by the SOM. Experimental results with anomaly and classification data are presented and discussed.A model that can detect anomalies, even when trained only with normal samples, and can learn from encounters with new anomalies is proposed. The model combines a negative selection algorithm and a self-organizing map (SOM) in an immune inspired architecture. One of the main advantages of the proposed system is that it is able to produce a visual representation of the self/non-self feature space, thanks to the topological two-dimensional map produced by the SOM. Experimental results with anomaly and classification data are presented and discussed.

This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organizing maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.

La trombosis venosa cerebral (TVC) es una patología infrecuente de gran severidad, con una mortalidad del 5%; que afecta predominantemente a mujeres. Existe escasa información disponible en nuestro país, por lo tanto, el objetivo fue evaluar las características de nuestra población con TVC.