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.

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.

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.

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 3N 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.

Quantum computing emerges as a pivotal solution to classical computing limitations in the post-Moore era, offering superior capabilities through qubits' unique properties of superposition, entanglement and interference. Actually, we are in the Noisy Intermediate-Scale Quantum era where quantum computing faces challenges such as limited resources and fault-tolerant circuit implementation. Scarce development resources hinder quantum algorithm and circuit progress, necessitating optimized compilers and methodologies. Presently, two dominant paradigms, general-purpose quantum computing and adiabatic computing, drive quantum advancements, with notable platforms including IBM's Qiskit and D-Wave. The ongoing research focuses on optimizing quantum circuits, particularly through novel gate implementations like Peres, TR, and GN gates, aimed at reducing T-count and T-depth. Preliminary results demonstrate promising reductions in these metrics, enhancing computational efficiency while adapting to the constraints of real quantum platforms. These endeavors aim to foster a repository of quantum routines, facilitating broader community access to quantum computational resources.

Quantum comparators are of significant importance within the realm of various quantum algorithms. In this work we improve the number of qubits needed to perform a comparison of two N-bit strings from 2N+1 qubits to N+2 . To achieve this, we resort to an encoding of the bits in which one qubit is not wasted for each bit entered. This encoding is based on the one proposed in the work of Pérez-Salinas et al. (2020), but in our case, we adapt it for use in arithmetic operations. We also use the implementation of the AND operation proposed by Gidney (2018) to reduce the number of T gates (significantly more expensive than the rest of the gates) necessary for comparison. The result is a circuit that equals the T-count of the best comparator for quantum computing currently available while halving the number of qubits required.

Quantum computing emerges as a pivotal solution to classical computing limitations in the post-Moore era, offering superior capabilities through qubits’ unique properties of superposition, entanglement and interference. Actually, we are in the Noisy Intermediate-Scale Quantum where quantum devices present challenges in circuit design. This work proposes optimized implementations of Binary Coded Decimal adders using Clifford+T gates. With a focus on mitigating the computational costs associated with T-gates and the error rates of CNOT gates, two distinct designs are presented. The first design prioritizes minimizing T-gate usage, leading to significant reductions in T-count and T-depth, while slightly decreasing the number of CNOT gates. The second design targets minimizing CNOT gate usage, resulting in a 15

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.

The so-called Hamming distance measures the difference between two binary strings A and B. In simplified form, it measures the number of changes in A to get B. This type of distance is very useful in classical computing in applications such as error correction. It is also advantageous in quantum computing, being for example widely used in quantum machine learning. Since current quantum computers have limited resources, this type of distance is particularly attractive because it can be computed using fewer qubits and operations than other distances such as Euclidean or Manhattan distances. In this paper, two circuits for calculating Hamming distances using exclusively Clifford+T gates are presented. The aim of both circuits is to reduce the quantum cost and number of T gates needed to compute the Hamming distance. The T gate is more expensive than the other gates, so this reduction will have a significant impact on the total cost of the circuits. Furthermore, the proposed circuits are implemented using only Clifford+T gates. The circuits implemented exclusively with this group of gates are compatible with proven error detection and correction codes.

One of the strategies to reduce the complexity of N -body simulations is the computation of the neighbour list. However, this list needs to be updated from time to time, with a high computational cost. This paper focuses on the use of quantum computing to accelerate such a computation. Our proposal is based on a well-known oracular quantum algorithm (Grover). We introduce an efficient quantum circuit to build the oracle that marks pairs of closed bodies, and we provide three novel algorithms to calculate the neighbour list under several hypotheses which take into account a-priori information of the system. We also describe a decision methodology for the actual use of the proposed quantum algorithms. The performance of the algorithms is tested with a statistical simulation of the oracle, where a fixed number of pairs of bodies are set as neighbours. A statistical analysis of the number of oracle queries is carried out. The results obtained with our simulations indicate that when the density of bodies is low, our algorithms clearly outperform the best classical algorithm in terms of oracle queries.

Quantum computing has emerged in recent years as an alternative to classical computing, which could improve the latter in solving some types of problems. One of the quantum programming models, Adiabatic Quantum Computing, has been successfully used to solve problems such as graph partitioning, traffic routing, and task scheduling. In this paper, the focus is on the scheduling of the problem of unrelated parallel machines, where the processing time of tasks on any of the available processing elements is known. Moreover, the proposed model is extended in two relevant aspects for this kind of problem: the existence of some degree of priority of tasks, and the introduction of a delay or penalty every time a processing unit or machine changes the type of task that executes.In all cases, the problem is expressed as Quadratic Unconstrained Binary Optimization, which can be subsequently solved using quantum annealers. The quantum nonlinear programming framework discussed in this work consists of three steps: quadratic approximation of cost function, a binary representation of parameter space, and solving the resulting Quadratic Unconstrained Binary Optimiza-tion on the quantum annealer platform D-Wave. One of the novelties in tackling this problem is the compaction of the model bearing in mind the repetitions of each task, to allow solving larger scheduling problems with the quantum resources available in the experimentation platform. An estimation of the number of qubits required in relation to the scheduling parameters is analyzed. The models have been implemented on the D-Wave platform and validated with respect to other traditional methods. Furthermore, the proposed extensions to consider priorities and to switch the delay of tasks have been analyzed using a case study.& COPY; 2023 Elsevier B.V. All rights reserved.

The dynamics of a tracer particle in a bath of quasi-hard colloidal spheres is studied by Langevin dynamics simulations and mode coupling theory (MCT); the tracer radius is varied from equal to up to seven times larger than the bath particles radius. In the simulations, two cases are considered: freely diffusing tracer (passive microrheology) and tracer pulled with a constant force (active microrheology). Both cases are connected by linear response theory for all tracer sizes. It links both the stationary and transient regimes of the pulled tracer (for low forces) with the equilibrium correlation functions; the velocity of the pulled tracer and its displacement are obtained from the velocity auto-correlation function and the mean squared displacement, respectively. The MCT calculations give insight into the physical mechanisms: At short times, the tracer rattles in its cage of neighbours, with the frequency increasing linearly with the tracer radius asymptotically. The long-time tracer diffusion coefficient from passive microrheology, which agrees with the inverse friction coefficient from the active case, arises from the transport of transverse momentum around the tracer. It can be described with the Brinkman equation for the transverse flow field obtained in extension of MCT, but cannot be recovered from the MCT kernel coupling to densities only. The dynamics of the bath particles is also studied; for the unforced tracer the dynamics is unaffected. When the tracer is pulled, the velocity field in the bath follows the prediction of the Brinkman model, but different from the case of a Newtonian fluid.

Quantum circuits performing arithmetic operations are critical in quantum computing because of the need for such operations in proven quantum algorithms. Although quantum computers are becoming increasingly resourceful, the number of qubits currently available is still limited. Furthermore, these qubits are heavily affected by internal and external noise. It has been proven that quantum circuits built using Clifford +T gates can be made fault tolerant. However, the use of the T gates comes at a very high cost. If the number of T gates used in a circuit is not optimized, the cost of the circuit will be increased excessively. As a consequence, it is essential to optimize the circuits so that they are as resource efficient as possible and also noise tolerant. This paper presents the design of a circuit to perform the multiplication of two integers. The circuit is built using only Clifford +T gates for compatibility with error detection and correction codes. It outperforms the circuits in the state of the art in terms of T count and T depth.

Random numbers are of vital importance in fields such as cyptography and scientific simulations. However, it is well known how difficult it is for classical computers to generate random numbers. This is not the case for quantum computers, which are able to genuinely generate random numbers thanks to the property of superposition and their counter-intuitive concept of measurement. However, despite the simplicity of designing a circuit that generates a random number between 0 and 2(N) - 1 (being N the number of available qubits), designing a quantum circuit to generate a number within a specific interval is far from trivial. This paper proposes a customizable circuit design to generate random numbers. The circuit is non-hardware dependent, it allows fault-tolerance, and it can be used by current quantum devices. Therefore, it is a valuable tool for all those quantum applications and algorithms that need to work with random numbers. Moreover, a comparator circuit has also been designed as part of this work. This comparator is the best currently available in the literature in terms of qubits, T-count, and T-depth. It is therefore a useful tool for any other circuit or algorithm where this operation is needed.

Quantum computing has emerged in recent years as an alternative to classical computing, which could improve the latter in solving some types of problems. One of the quantum programming models, Adiabatic Quantum Computing, has been successfully used to solve problems such as graph partitioning, traffic routing and task scheduling. Specifically, in this paper we focus on the scheduling on unrelated parallel machines problem. It is a workload-balancing problem where the processing time of any procedure executed on any of the available processing elements is known. Here, the problem is expressed as Quadratic Unconstrained Binary Optimisation, which can be subsequently solved using quantum annealers. The quantum nonlinear programming framework discussed in this work consists of three steps: quadratic approximation of cost function, binary representation of parameter space, and solving the resulting Quadratic Unconstrained Binary Optimisation. One of the novelties in tackling this problem has been to compact the model bearing in mind the repetitions of each task, to make it possible to solve larger scheduling problems.

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.

G. Ortega1J.J. Moreno1E.M. Garzón1F.J. Orts2L.M. Donaire1L.O. López1C.J. Díaz-Carrasco1M. Lupión1A. Redondo1

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.

Development of composite indicators is a challenging task given that sustainability indices are strongly dependent on how the sub-indicators are weighted. This is because relative indicator weights may significantly differ based on the chosen weighting methods used in the analysis. There is hardly any study that has paid attention to this issue so far. Therefore, this paper aims to fill this gap in the literature by searching the robustness of selected weighting methods, i.e. entropy-weight (EW), principal component analysis (PCA), machine learning approaches (random forest-RF), regression analysis (RA) and benefit-of-the-doubt (BOD) when constructing a composite indicator. To research the current sustainability performance of European regions, the present study focuses on the Territorial Quality of Life Index—initially proposed by the ESPON Programme—that are aligned with the specific targets of the Sustainable Development Goals of the 2030 Agenda. The methods to construct composite indicators include stages of data preparation (including the estimation of missing values with random forest method), normalization, statistical transformation of raw data, reduction of indicators in order to ease public communication (using the PCA method) and data interpretation, weighting of the sub-indicators using EW, PCA, RF, RA and BOD methods and their linear weighted aggregation, and checking for robustness and sensitivity. The results suggest that there are significant differences in the rank and spatial distribution of composite indicators based on the use of different weighting methods considered in the analysis. The results from sensitivity analysis support the robustness of entropy-weight method among others. The methodology used in the current analysis can be adapted to other study areas and regions internationally. The findings showed that Eastern European countries and some Mediterranean countries have relatively lower index values compared to other European regions; therefore, policy and planning actions are needed covering these regions specifically.