Quantum Machine Learning (QML) combines principles of quantum computing with traditional Machine Learning (ML) to explore computational advantages in data processing and model efficiency. With the rise of Noisy Intermediate-Scale Quantum (NISQ) devices, hybrid quantum–classical approaches are gaining momentum, especially in domains requiring high precision such as healthcare. In this work, we investigate whether hybrid quantum computing can enhance certain aspects of classical ML, specifically in dataset balancing and the complexity of the neural network involved in training. To this end, we use the Indian Liver Patient Dataset as a case study to determine the presence of liver disease. We present the methodology for developing ‘QML-Liver’, a hybrid approach that seamlessly integrates classical and QML techniques. This includes data preprocessing, model design, and optimal configuration. Our results demonstrate that ‘QML-Liver’ improves key performance metrics, such as accuracy and F1-Score. Additionally, we successfully reduce the number of required qubits to just two, making practical deployment more feasible. These findings underscore the potential of QML for medical diagnostics, particularly in the NISQ era.

• Hybrid quantum-classical model for Spanish hate speech detection. • Two-phase training stabilizes quantum circuit optimization. • Competitive with transformers; best results on HaterNet dataset. • Consistently outperforms classical and recurrent baselines. • Demonstrates viability of quantum NLP in real-world tasks. Hate speech detection in social media remains a pressing challenge in natural language processing, particularly for languages such as Spanish where annotated resources are limited. This work proposes a hybrid quantum-classical neural architecture that combines bidirectional gated recurrent units with attention and a variational quantum circuit used as a non-linear classifier. The model is trained in two phases: first the recurrent and attention-based layers are optimized to produce stable representations, then these are frozen and a quantum circuit is fine-tuned for classification. Evaluation on two benchmark corpora, HatEval and HaterNet, shows that the proposed hybrid approach achieves competitive performance with strong transformer baselines such as BETO and XLM-R, while consistently outperforming traditional machine learning and recurrent neural models. On HaterNet, the proposed model performs on par with, and in some metrics slightly better than, the transformer baselines, whereas on HatEval it attains slightly lower scores. Its strength lies in detecting hate speech under class imbalance, as reflected in solid F1 scores for the hate speech class. These findings provide an initial empirical assessment of quantum-enhanced NLP in a realistic hate speech detection scenario and suggest promising directions for further study as quantum hardware matures, without constituting evidence of quantum advantage.

ABSTRACT Introduction The rapid growth of quantum computing frameworks such as Qiskit, Cirq, and Amazon Braket has accelerated quantum software development but has also introduced fragmentation in tools, syntax, and workflows. Methods This paper presents a lightweight and extensible interface for building quantum circuits from tokenized text input, enabling rapid prototyping and cross‐framework compatibility. The proposed system defines a simple domain‐specific language composed of quantum instruction tokens (e.g., H0, CNOT0‐1) that are parsed into a unified intermediate representation. This intermediate form is then translated into executable quantum circuits across multiple backends using a modular translator architecture. In addition, the system supports user‐defined gate decompositions through a configurable mapping mechanism. Results The proposed system is implemented in Python and evaluated across three major quantum software development kits. Experimental results demonstrate consistent and correct circuit generation across all supported backends. Conclusions The approach promotes abstraction, reusability, and readability in the quantum software development lifecycle, providing a practical solution to mitigate fragmentation across quantum programming frameworks.

Abstract Quantum squaring circuits play a critical role in many quantum algorithms; however, most existing designs incur a significant qubit overhead due to the loss of input states and excessive use of ancillary qubits. In this work, we introduce a qubit-efficient quantum circuit for integer squaring that achieves a linear qubit cost of only 3 N qubits for an N -bit input, significantly outperforming state-of-the-art designs that scale quadratically in terms of qubits. Our approach reintegrates the input operand after computation, enabling the uncomputation of intermediate results and efficient recycling of ancilla qubits. This reversible strategy prevents the retention of redundant information, which is a common limitation of prior works. The comparative analysis confirms the scalability and practicality of our design for qubit-constrained quantum hardware, offering a promising solution for arithmetic operations in resource-limited quantum environments.

This work aims to present an innovative methodology to be followed in the subject “Advanced Computing”, part of the Master's program in Computer Science at the University of Almeria. The methodology seeks to enhance students' engagement with their learning process through modern and effective approaches. Simultaneously, it aims to expand their practical experience through the use of cutting-edge tools for rapid application development, such as Spring Boot and Angular, and High Performance Computing techniques applied using CUDA. The primary methodological approach adopted in this course is based on the flipped classroom methodology. This approach will be implemented in conjunction with a real scientific case involving a physical application of microrheology, providing students with a practical and engaging learning experience. Subsequently, the course delves into tools for developing a web application that serves as a visual interface for the generated data, employing rapid application development techniques. Throughout the course, brief theory blocks will be provided as video lectures uploaded by the professor to explain computational tools and the scientific case. However, classes will primarily focus on practical development, where students are provided with a base project from the outset, allowing them sufficient time to complete it independently. As part of the flipped classroom model, the professor will offer support if needed. Additionally, students will be assigned tasks to implement distinct and straightforward features, fostering both their confidence and creativity as independent computer engineers. The course also incorporates AI-driven programming assistance to promote modern productivity methodologies, avoiding common pitfalls that may limit students' programming skills.

Abstract Quantum comparators hold substantial significance in the scientific community as fundamental components in a wide array of algorithms. In this research, we present an innovative approach where we explore the realm of comparator circuits, specifically focussing on three distinct circuit designs present in the literature. These circuits are notable for their use of T-gates, which have gained significant attention in circuit design due to their ability to enable the utilisation of error-correcting codes. However, it is important to note that T-gates come at a considerable computational cost. One of the key contributions of our work is the optimisation of the quantum gates used within these circuits. We articulate the proposed circuits employing Clifford+T gates, facilitating error correction code implementation. Additionally, we minimise T-gate usage, thereby reducing computational costs and fortifying circuit robustness against errors and environmental disturbances-essential for mitigating the effects of internal and external noise. Our methodology employs a bottom-up examination of comparator circuits, initiating with a detailed study of their gates. Subsequently, we systematically dissect the functions of these gates, thereby advancing towards a comprehensive understanding of the circuit’s overall functionality. This meticulous examination forms the foundation of our research, enabling us to identify areas where optimisations can be made to improve their performance.

While using High-Performance Computing (HPC) for precise and accurate air quality forecasts is a common issue, similar services devoted to marine pollution in coastal areas remain challenging. This paper presents Water quality Community Model Plus Plus (WaComM++) leveraging a parallelization schema enabling the users to run it on heterogeneous parallel architectures. We evaluated the proposed model under several execution approaches using a real-world application for pollutants forecast in the Gulf of Napoli (Campania, Italy). As a result, WaComM++ has produced results 657K times faster than the sequential run (taking into account the Particles' Outer Cycle and not considering the particle domain distribution) when using distributed and shared memory with multi-GPUs dealing with about 25 million particles.

Abstract The intrinsic high parallelism and entanglement characteristics of quantum computing have made quantum image processing techniques a focus of great interest. One of the most widely used techniques in image processing is segmentation, which in one of their most basic forms can be carried out using thresholding algorithms. In this paper, a fault-tolerant quantum dual-threshold algorithm has been proposed. This algorithm has been built using only Clifford+T gates for compatibility with error detection and correction codes. Because fault-tolerant implementation of T gates has a much higher cost than other quantum gates, our focus has been on reducing the number of these gates. This has allowed adding noise tolerance, computational cost reduction, and fault tolerance to the state-of-the-art dual-threshold segmentation circuits. Since the dual-threshold image segmentation involves the comparison operation, as part of this work we have implemented two full comparator circuits. These circuits optimize the metrics T-count and T-depth with respect to the best circuit comparators currently available in the literature.

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.

Abstract Adders are one of the most interesting circuits in quantum computing due to their use in major algorithms that benefit from the special characteristics of this type of computation. Among these algorithms, Shor’s algorithm stands out, which allows decomposing numbers in a time exponentially lower than the time needed to do it with classical computation. In this work, we propose three fault-tolerant carry lookahead adders that improve the cost in terms of quantum gates and qubits with respect to the rest of quantum circuits available in the literature. Their optimal implementation in a real quantum computer is also presented. Finally, the work ends with a rigorous comparison where the advantages and disadvantages of the proposed circuits against the rest of the circuits of the state of the art are exposed. Moreover, the information obtained from such a comparison is summarized in tables that allow a quick consultation to interested researchers.

Dynamic programming (DP) approaches, in particular value iteration, is often seen as a method to derive optimal policies in inventory management. The challenge in this approach is to deal with an increasing state space when handling realistic problems. As a large part of world food production is thrown out due to its perishable character, a motivation exists to have a good look at order policies in retail. Recently, investigation has been introduced to consider substitution of one product by another, when one is out of stock. Taking this tendency into account in a policy requires an increasing state space. Therefore, we investigate the potential of using GPU platforms in order to derive optimal policies when the number of products taken into account simultaneously is increasing. First results show the potential of the GPU approach to accelerate computation in value iteration for DP.

This work is devoted to presenting some strategies aimed at incrementing the students' commitment with their formative process and, at the same time, achieving a satisfactory evaluative procedure. The strategies described in this work have been applied in the subject "Computer Structure and Technology", which corresponds to the BSc in Computer Science syllabus at the University of Almería. Broadly speaking, the subject delves into internal aspects of computer operation, such as several devices found in the computer data path, memory organization, and the control unit. These issues are frequently very unpopular among students because they are not familiar with the deepest level of the operations and, therefore, they usually need to dedicate a huge amount of time and effort to understand them. For these reasons, a student's level of competency at the endo of the course is not usually very high and many students even drop out of the subject in its early stages. To overcome this situation, we have introduced two novelties in the learning methodology. On the one hand, we have taken advantage of possibilities that the Learning Management System of our university (Blackboard) offers using Learning Analytics to find out what activities are more relevant from an academic result point of view. On the other hand, we have proposed evaluative mentoring to personally deal with the students' necessities. Thus, the methodology combines the advantages of both, online and on-site activities. As an obtained result, we have studied the validity of the methodological strategies, detecting the issues that have more impact on the assessment results. This fact is very helpful to focus our attention on these issues in the following academic years. Moreover, we have demonstrated a significative improvement in students' competency levels with respect to previous academic years in the same subject and other subjects of the same course.

Summary The resolution of the 3D Helmholtz equation is required in the development of models related to a wide range of scientific and technological applications. For solving this equation in complex arithmetic, the biconjugate gradient (BCG) method is one of the most relevant solvers. However, this iterative method has a high computational cost because of the large sparse matrix and the vector operations involved. In this paper, a specific BCG method, adapted for the regularities of the Helmholtz equation is presented. This BCG is based on the implementation of a novel format (named ‘Regular Format’) that allows the storage of the large sparse matrix involved in the sparse matrix vector product in a compact form. The contribution of this work is twofold: (1) decreasing the memory requirements of the 3D Helmholtz equation using the ‘Regular Format’ and (2) speeding up the resolution of the equation using high performance computing resources. A hybrid Message Passing Interface (MPI)‐graphics processing unit CUDA GPU parallelization that is capable of solving complex problems in short time has carried out (Fast‐Helmholtz). Fast‐Helmholtz combines optimizations at Message Passing Interface and GPU levels to reduce communications costs and to improve the exploitation of GPU architecture. This strategy makes it possible to extend the dimension of the Helmholtz problem to be solved, thanks to the relevant reduction of memory requirements and runtime. Copyright © 2014 John Wiley & Sons, Ltd.

Sparse matrix matrix (SpMM) multiplication is involved in a wide range of scientific and technical applications. The computational requirements for this kind of operation are enormous, especially for large matrices. This paper analyzes and evaluates a method to efficiently compute the SpMM product in a computing environment that includes graphics processing units (GPUs). Some libraries to compute this matricial operation can be found in the literature. However, our strategy (FastSpMM) outperforms the existing approaches because it combines the use of the ELLPACK-R storage format with the exploitation of the high ratio computation/memory access of the SpMM operation and the overlapping of CPU–GPU communications/computations by Compute Unified Device Architecture streaming computation. In this work, FastSpMM is described and its performance evaluated with regard to the CUSPARSE library (supplied by NVIDIA), which also includes routines to compute SpMM on GPUs. Experimental evaluations based on a representative set of test matrices show that, in terms of performance, FastSpMM outperforms the CUSPARSE routine as well as the implementation of the SpMM as a set of sparse matrix vector products.

The detection of mesoscale oceanic structures, such as upwellings or eddies, from satellite images has significance for marine environmental studies, coastal resource management, and ocean dynamics studies. Nevertheless, there is a lack of tools that allow us to retrieve automatically relevant mesoscale structures from large satellite image databases. This paper focuses on the development and validation of a content-based image retrieval system to classify and retrieve oceanic structures from satellite images. The images were obtained from the National Oceanic and Atmospheric Administration satellite's Advanced Very High Resolution Radiometer sensor. The study area is about W2°- 21°, N19°- 45°. This system conducts labeling and retrieval of the most relevant and typical mesoscale oceanic structures, such as upwellings, eddies, and island wakes located in the Canary Islands area and in the Mediterranean and Cantabrian seas. Our work is based on several soft computing technologies such as fuzzy logic and neurofuzzy systems.

A wide range of applications in engineering and scientific computing are based on the computation of matrices products, where one of them is sparse. The computational requirements of these operations are very high when dimensions of the matrices increase. The goal of this work is the acceleration of the sparse matrix matrix product (SpMM) on Graphics Processing Units (GPUs). The operation SpMM can be computed by a set of sparse matrix vector operations (SpMV). However, this approach does not reach optimal performance because it cannot benefit from the large value of the ratio computation/memory access associated to the SpMM operation. In this work a routine called FastSpMM is described and its performance evaluated. FastSpMM can be considered as an extension of the ELLRT routine to compute SpMV on GPUs which is based on the ELLPACK-R storage format for sparse matrices. FastSpMM combines the high ratio computation/memory access with the advantages of ELLR-T to exploit the GPU architecture. The CUSPARSE library, supplied by NVIDIA, which also includes routines to compute SpMM on GPUs is used in this work as a reference for performance comparison. Experimental evaluations based on a representative set of test matrices show that FastSpMM outperforms the corresponding CUSPARSE routine in terms of performance.