I hold a Degree in Computer Engineering (2024) from the University Pablo de Olavide, where I am currently pursuing a Master’s Degree in Computer Engineering. Since October 2023, I have been working as a Researcher at the Data Science and Big Data Lab. My work focuses on the development of methods for the optimization of quantum circuits within the field of Quantum Machine Learning (QML), combining advanced techniques in software engineering, artificial intelligence, and quantum computing.
I am particularly interested in addressing complex challenges such as circuit simplification, noise mitigation, and the integration of QML into real-world applications. My current research aims to contribute to the practical advancement of quantum-enhanced machine learning and the broader field of data science.
In addition to my research, I have started teaching as an Interim Substitute Lecturer at the School of Engineering of the University Pablo de Olavide. I teach in the Degree in Computer Engineering, specializing in Information Systems, during the first semester of 2024–2025.
Publications
2026 |
D. Martín-Pérez and F. Rodríguez-Díaz and A. Troncoso and F. Martínez-Álvarez SOCO 21st Conference on Computing Systems and Applications, Communications in Computer and Information Science 2026. @conference{SOCO26_Martin,Quantum Machine Learning has shown significant promise but suffers from catastrophic forgetting in continual learning scenarios. In this work, a hybrid classical-quantum approach to mitigate catastrophic forgetting is proposed, based on a rigorous evaluation of how the topology of Variational Quantum Circuits interacts with Dark Experience Replay. By constructing a controlled Noisy Intermediate-Scale Quantum simulation environment based on the Fashion-MNIST dataset, the forgetting dynamics are analyzed across three distinct quantum circuit ansatzes: Highly Expressive, Hardware-Efficient and Tree Tensor Networks. Extensive evaluation demonstrates that, when relying on naive fine-tuning, dense quantum entanglement structures exhibit more than 30% catastrophic forgetting across sequential tasks. However, by using Dark Experience Replay specifically, the hybrid network effectively anchors past distributions via logit distillation, reducing catastrophic forgetting margins to under 5% and robustly retaining previously acquired knowledge. This study provides an empirical foundation for deploying scalable lifelong learning on Noisy Intermediate-Scale Quantum devices using state-of-the-art memory buffers. |
F. Rodríguez-Díaz and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez Gate Value Quantification for Efficient Quantum Machine Learning Circuits Conference SOCO 21st Conference on Computing Systems and Applications, Communications in Computer and Information Science 2026. @conference{SOCO26_Rodriguez,Quantum machine learning circuits are limited by depth, the number of two-qubit gates, and execution cost, which motivates the elimination of operations that contribute little to predictive performance. This paper presents a gate value methodology that quantifies the contribution of individual gates and iteratively derives reduced circuit configurations to improve the trade-off between accuracy and time. Gate relevance is calculated using two complementary strategies: a fidelity-based strategy and an entanglement-based strategy. Experimental configurations are ranked by best accuracy and best time. Finally, experiments with several binary classification datasets in an ideal simulation environment show that moderate gate removal can preserve, and sometimes improve, accuracy while reducing execution time. |
H. Affou and F. Rodríguez-Díaz and F. Martínez-Álvarez and J. M. López-Guede IWINAC 11th International Work-Conference on the Interplay Between Natural and Artificial Computation, Lecture Notes in Computer Science 2026. @conference{IWINAC26_Affou,Intelligent traffic signal control has become a central research topic in urban mobility, aiming to reduce congestion and improve operational efficiency under increasingly complex traffic conditions. Although classical evolutionary algorithms have shown strong performance in traffic signal optimization, the practical integration of quantum computing into evolutionary processes remains largely unexplored, particularly under realistic microscopic traffic simulation. This paper presents a novel quantum genetic evolutionary approach for traffic signal timing optimization, implemented in a real signalized roundabout in Vitoria-Gasteiz, Spain, using the Simulation of Urban Mobility. The proposed method combines a classical GA for global exploration with a Grover-inspired quantum module embedded directly within the evolutionary cycle. Unlike conventional hybrid schemes in which quantum routines are treated as external solvers or purely simulated components, the proposed quantum GA is executed on real IBM Quantum hardware under NISQ conditions. Comparative experiments against fixed time control and a purely classical GA demonstrate substantial reductions in delay and improved convergence stability. |
F. Rodríguez-Díaz and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez A Survey of Quantum Machine Learning: Foundations, Algorithms, Frameworks, Data and Applications Journal Article In: ACM Computing Surveys, vol. 58, iss. 4, pp. 1-35, 2026. @article{CSUR2025,Quantum machine learning combines quantum computing with machine learning to solve complex computational problems more efficiently than classical approaches. This survey provides an introduction to the foundations, algorithms, frameworks, data and applications of quantum machine learning, serving as a resource for researchers and practitioners. We begin by reviewing existing surveys to identify gaps that this work addresses, followed by a detailed discussion of the foundational principles of quantum mechanics and machine learning essential for quantum machine learning. Key algorithms are examined, highlighting their mechanisms, advantages, and applications across various domains. Current frameworks and platforms for implementing quantum machine learning algorithms are explored, emphasizing their unique features and suitability for different contexts. Existing quantum datasets for practical usage are also reported and commented on. This survey also reviews over 135 articles, categorized into theoretical and practical contributions, to identify key advances, limitations, and application areas within quantum machine learning. Critical challenges such as hardware limitations, error rates, and scalability are analyzed to detect the obstacles that must be addressed for practical deployment. By synthesizing these elements into a structured overview, this survey aims at serving as both an introduction and a guide for advancing research and development in this disruptive field. |
A. Vellinger and F. Rodríguez-Díaz and F. Divina and J. F. Torres Forecasting livestock activity through interpretable neuroevolutionary transfer learning Journal Article In: Logic Journal of the IGPL, vol. 34, pp. jzaf034, 2026. @article{VELLINGER26,In this paper, we describe a neuroevolutionary approach to livestock activity forecasting, specifically targeting the prediction of Iberian pigs movements. We successfully integrated Transfer Learning to save computational time and used an Explainable Artificial Intelligence technique to provide valuable insights from the model predictions. Inspired by previous work, we employ Deep Evolutionary Network Structured Representation to optimize both Long Short-Term Memory networks and Convolutional Neural Networks using genetic algorithms and dynamic structured grammatical evolution, and we compare the results with other commonly used approaches for time series forecasting. Experimental results demonstrate the superior performance of the proposed Long Short-Term Memory models over more traditional methods, highlighting their precision and consistency in predicting livestock activities. Furthermore, the application of Explainable Artificial Intelligence techniques enable to gain a deeper understanding and trust in AI-driven decisions within precision livestock farming. |
2025 |
C. Herruzo-Lodeiro and F. Rodríguez-Díaz and A. Troncoso and M. Martínez-Ballesteros SAC 40th ACM/SIGAPP Symposium on Applied Computing, 2025. @conference{SAC2025, |
I. Rojas-García and F. Rodríguez-Díaz and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez A Practical Implementation of Quantum LSTM Using Qiskit and PyTorch Conference SOCO 20th Conference on Computing Systems and Applications, Communications in Computer and Information Science 2025. @conference{SOCO25_Rojas,Quantum computing is emerging as a promising tool to enhance classical machine learning models, especially in areas that involve complex temporal dynamics and high-dimensional data. This paper presents a practical implementation of a Quantum Long Short-Term Memory network. This hybrid architecture integrates parameterized quantum circuits into the gating mechanisms of a classical Long Short-Term Memory network. Built using PyTorch and Qiskit, our model enables end-to-end simulation, training, and analysis of quantum-enhanced recurrent networks. We design modular quantum circuits that interact with the Long Short-Term Memory’s internal states and evaluate their performance in processing synthetic sequential data. The architecture includes visualization and diagnostic tools to explore hidden state evolution, final memory values, and gate behaviors. Furthermore, we introduce an automated performance optimization framework that compares multiple Quantum Long Short-Term Memory network configurations using varying key quantum parameters such as qubit count and entanglement depth. Our findings demonstrate the feasibility of constructing interpretable, tunable quantum-classical sequence models using widely available tools. This work contributes a fully reproducible platform for experimentation and lays the groundwork for future research in scalable quantum recurrent architectures. |
D. Martín-Pérez and F. Rodríguez-Díaz and A. Troncoso and F. Martínez-Álvarez Comparative Study of Hybrid Classical–Quantum Transfer Learning Conference SOCO 20th Conference on Computing Systems and Applications, Communications in Computer and Information Science 2025. @conference{SOCO25_Martin,The combination of classical deep neural networks with quantum circuits has recently attracted attention as a promising paradigm for the current era of noisy intermediate-scale quantum (NISQ) technology. In this research, we perform a detailed comparative analysis of three classical Convolutional Neural Network architectures (ResNet18, VGG16, and MobileNetV2) combined with two distinct variational quantum circuits (VQC) acting as classifiers. To carefully assess the quantum contribution, we also introduce a purely classical baseline with an equivalent parameter budget. Each model is trained on the Hymenoptera dataset, and performance is checked based on validation accuracy and training time. Our findings show the important trade-offs between computational cost and model complexity, offering practical guidance for future hybrid classical-quantum applications and clarifying the performance benefits of different quantum circuit designs. |
2024 |
F. Rodríguez-Díaz and A. M. Chacón-Maldonado and A. R. Troncoso-García and G. Asencio-Cortés Explainable Olive grove and Grapevine pest forecasting through machine learning-based classification and regression Journal Article In: Results in Engineering, vol. 24, pp. 103058, 2024. @article{RODRIGUEZ24,Pests significantly impact agricultural productivity, making early detection crucial for maximizing yields. This paper explores the use of machine learning models to predict olive fly and red spider mite infestations in Andalusia. Four datasets on crop phenology, pest populations, and damage levels were used, with models developed using the Python package H20, which focuses on interpretability through SHAP values and ICE plots. The results showed high precision in predicting pest outbreaks, particularly for the olive fly, with minimal differences between models using feature selection. In the vineyard dataset, the selection of characteristics improved the performance of the model by reducing the MAE and increasing R2. Explainability techniques identified solar radiation and wind direction as key factors in olive fly predictions, while past pest occurrences and wind velocity were influential for red spider mites, providing farmers with actionable insights for timely pest control. |
F. Rodríguez-Díaz and J. F. Torres and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez CAEPIA Conference of the Spanish Association for Artificial Intelligence, Lecture Notes in Artificial Intelligence 2024. @conference{RODRIGUEZ-DIAZ24b,Quantum computing holds great promise for enhancing machine learning algorithms, particularly by integrating classical and quantum techniques. This study compares two prominent quantum development frameworks, Qiskit and Pennylane, focusing on their suitability for hybrid quantum-classical support vector machines with quantum kernels. Our analysis reveals that Qiskit requires less theoretical information to be used, while Pennylane demonstrates superior performance in terms of execution time. Although both frameworks exhibit variances, our experiments reveal that Qiskit consistently yields superior classification accuracy compared to Pennylane when training classifiers with quantum kernels. Additionally, our results suggest that the performance of both frameworks remains stable for up to 20 qubits, indicating their suitability for practical applications. Overall, our findings provide valuable insights into the strengths and limitations of Qiskit and Pennylane for hybrid quantum-classical machine learning. |
C. Moral-Turón and G. Asencio-Cortés and F. Rodriguez-Diaz and A. Rubio and A. G. Navarro and A. M. Brokate-Llanos and A. Garzón and M. J. Muñoz and A. J. Pérez-Pulido ASACO: Automatic and Serial Analysis of CO-expression to discover gene modifiers with potential use in drug repurposing Journal Article In: Briefings in Functional Genomics, vol. 23, no. 4, pp. 484-494, 2024, ISSN: 2041-2657. @article{10.1093/bfgp/elae006,Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge. If several genes have a similar expression profile across heterogeneous transcriptomics experiments, they could be functionally related. These associations are usually useful for the annotation of uncharacterized genes. In addition, the search for genes with opposite expression profiles is useful for finding negative regulators and proposing inhibitory compounds in drug repurposing projects. Here we present a new web application, Automatic and Serial Analysis of CO-expression (ASACO), which has the potential to discover positive and negative correlator genes to a given query gene, based on thousands of public transcriptomics experiments. In addition, examples of use are presented, comparing with previous contrasted knowledge. The results obtained propose ASACO as a useful tool to improve knowledge about genes associated with human diseases and noncoding genes. ASACO is available at http://www.bioinfocabd.upo.es/asaco/. |