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.

Qubits are the minimum unit of information in quantum computing. As current quantum devices have a very limited number of available qubits, finding circuits that optimize their use is crucial for this computing paradigm. Optimizing the qubits needed to implement a quantum circuit is not trivial, as these circuits have important restrictions such as the impossibility of copying values or the requirement that the computation done in them must always be reversible. This paper presents a framework for optimizing circuits for quantum computing. The framework is able to obtain, given a quantum circuit, an equivalent one that is more efficient in terms of number of qubits. In particular, the framework has applicability in quantum circuits dedicated to arithmetic operations such as addition or multiplication. As a representative case study, it is applied to a reversible comparator circuit, obtaining a design that reduces the number of qubits and controlled gates compared to other reversible comparators reported in the recent literature. As part of the work, a public repository is included with the necessary code to simplify any other circuit.

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.

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.