Abstract Quantum Machine Learning (QML) holds the promise of improving conventional machine learning, but the conditions under which an advantage can be obtained are still unclear. Although many studies have been conducted in the literature, there is no conclusive evidence that allows us to determine which types of classical datasets or which problem complexities benefit from the use of quantum techniques. Moreover, there have been contradicting findings when dealing with small or unbalanced datasets. In order to systematically approach this challenge and clarify the need for quantum properties in specific tasks, we propose the adoption of a framework capable of exploring the solution space of quantum feature maps and providing solid evidence of their potential advantages. Our framework comprises three main components: (a) the construction of datasets designed to highlight the specific characteristics that are expected to be relevant in quantum machine learning tasks; (b) the definition of metrics that serve as proxies for quantum kernels with desirable properties; and (c) an evolutionary-guided search for quantum feature maps maximizing those metrics. When using this framework with sensible parameter choices, we obtain results suggesting that some previous studies may have reported overfitted outcomes. This shows that justifying the need for quantum mechanical properties might be beyond the actual scope of conventional classical tasks, since there is no clear quantum feature that contributes to the gain shown by some of the QML techniques most commonly applied in the literature.

Abstract In this paper, we address the challenge of multivariate time-series forecasting through quantum-enhanced machine learning architectures. We propose adaptation strategies that extend variational quantum circuit models (VQC), traditionally constrained to univariate data, toward the multivariate setting, exploring both purely quantum and hybrid quantum-classical formulations. First, we extend and benchmark several VQC-based and hybrid models to systematically evaluate their ability to capture cross-variable dependencies. Building on these foundations, we introduce the iQTransformer, a novel quantum transformer architecture that integrates a quantum self-attention mechanism within the iTransformer framework, enabling a quantum-native representation of inter-variable relationships. Finally, we present a comprehensive empirical evaluation on both synthetic and real-world datasets, demonstrating that quantum-enhanced models can achieve competitive or superior forecasting accuracy with fewer trainable parameters and faster convergence than state-of-the-art classical and quantum baselines. These findings highlight the potential of quantum-enhanced architectures as efficient and scalable tools for advancing multivariate time-series forecasting.

Abstract Quantum machine learning, positioned at the convergence of quantum computing and artificial intelligence, is an emerging and highly promising field, primarily due to its potential to enhance the performance of classical machine learning systems. As this area is developing at an exceptionally rapid pace, it is essential to remain up to date with the latest advancements and research. This paper introduces pLazyQML, a software package designed to accelerate, automate, and streamline experimentation with quantum machine learning models on classical computers. pLazyQML reduces the complexity and time required for developing and testing quantum-enhanced machine learning models. Comprehensive experiments on established models and datasets demonstrate the efficiency, scalability, and workflow simplification provided by pLazyQML, making it a valuable tool for researchers and practitioners in quantum machine learning.

Abstract Current quantum computers have a limited number of resources and are heavily affected by internal and external noise. Therefore, small, noise-tolerant circuits are of great interest. With regard to circuit size, it is especially important to reduce the number of required qubits. Concerning to fault-tolerance, circuits entirely built with Clifford+T gates allow the use of error correction codes. However, the T-gate has an excessive cost, so circuits with a high number of T-gates should be avoided. This work focuses on optimising in such terms an operation that is widely used in larger circuits and algorithms: the calculation of the absolute-value of two’s complement encoded integers. The proposed circuit halves the number of required T gates with respect to the best circuit currently available in the literature. Moreover, our circuit requires at least 2 qubits less than the other circuits for such an operation.

Eco-efficiency in hearing aids is an important issue, related to the maximization of the battery life. In order to minimize the power consumption, the embedded digital signal processor works at very low clock rates, constraining the implementation of signal processing techniques. The implemented algorithms can only use a small number of instructions per second and a small amount of memory. One of the main algorithms implemented in nowadays hearing aids is the voice activity detection algorithm, useful for several noise reduction and speech enhancement algorithms. The objective of this paper is the study of the implementation of voice activity detection algorithms in hearing aids using tailored fuzzy features, taking into account the optimization of the available resources.

Precision Agriculture entails the appropriate management of the inherent variability of soil and crops, resulting in an increase of economic benefits and a reduction of environmental impact. However, site-specific treatments require maps of the soil variability to identify areas of land that share similar properties. In order to produce these maps, we propose a cost-efficient method that combines clustering algorithms with publicly available satellite imagery. The method does not require exploring the parcels with any special equipment or taking samples of the soil for laboratory analysis. The proposed method was tested in a case study for three vineyard parcels with topographical dissimilarities. The study compares different spectral and thermal bands from the Landsat 8 satellite as well as vegetation and moisture indices to determine which one produces the best clustering. The experimental results seem promising for identification of agricultural management zones. The findings suggest that thermal bands produce better clustering than those based on the NDVI index.

In this paper we study some properties of the polytope of belief functions on a finite referential. These properties can be used in the problem of identification of a belief function from sample data. More concretely, we study the set of isometries, the set of invariant measures and the adjacency structure. From these results, we prove that the polytope of belief functions is not an order polytope if the referential has more than two elements. Similar results are obtained for plausibility functions.

Feature Selection is an important task within Text Categorization, where irrelevant or noisy features are usually present, causing a lost in the performance of the classifiers. Feature Selection in Text Categorization has usually been performed using a filtering approach based on selecting the features with highest score according to certain measures. Measures of this kind come from the Information Retrieval, Information Theory and Machine Learning fields. However, wrapper approaches are known to perform better in Feature Selection than filtering approaches, although they are time-consuming and sometimes infeasible, especially in text domains. However a wrapper that explores a reduced number of feature subsets and that uses a fast method as evaluation function could overcome these difficulties. The wrapper presented in this paper satisfies these properties. Since exploring a reduced number of subsets could result in less promising subsets, a hybrid approach, that combines the wrapper method and some scoring measures, allows to explore more promising feature subsets. A comparison among some scoring measures, the wrapper method and the hybrid approach is performed. The results reveal that the hybrid approach outperforms both the wrapper approach and the scoring measures, particularly for corpora whose features are less scattered over the categories.

The generation of fuzzy measures is an important question arising in the practical use of these operators. In this paper, we deal with the problem of developing a random generator of fuzzy measures. More concretely, we study some of the properties that any random generator should satisfy. These properties lead to some theoretical problems concerning the group of isometries that we tackle in this paper for some subfamilies of fuzzy measures.

Text categorization, which consists of automatically assigning documents to a set of categories, usually involves the management of a huge number of features. Most of them are irrelevant and others introduce noise which could mislead the classifiers. Thus, feature reduction is often performed in order to increase the efficiency and effectiveness of the classification. In this paper, we propose to select relevant features by means of a family of linear filtering measures which are simpler than the usual measures applied for this purpose. We carry out experiments over two different corpora and find that the proposed measures perform better than the existing ones.

Abstract Text Categorization is the process of assigning documents to a set of previously fixed categories. A lot of research is going on with the goal of automating this time‐consuming task. Several different algorithms have been applied, and Support Vector Machines (SVM) have shown very good results. In this report, we try to prove that a previous filtering of the words used by SVM in the classification can improve the overall performance. This hypothesis is systematically tested with three different measures of word relevance, on two different corpus (one of them considered in three different splits), and with both local and global vocabularies. The results show that filtering significantly improves the recall of the method, and that also has the effect of significantly improving the overall performance.