I hold a Bachelor’s Degree in Computer Engineering in Information Systems (2025) from the Universidad Pablo de Olavide in Seville, Spain. I am currently pursuing a Master’s Degree in Artificial Intelligence Research at the Universidad Internacional Menéndez Pelayo, with an expected completion in 2026.
Since the beginning of 2025, I have been working as a Research Support Specialist at the Data Science and Big Data Research Lab. My work is centered on the field of Quantum Computing, with a specific focus on developing hybrid quantum-classical transfer learning models and advancing the principles of green quantum computing.
In addition to my research, I will be involved in teaching activities, delivering a micro-credential course on the fundamentals of quantum computing.
My primary areas of interest include quantum transfer learning, green and sustainable quantum computing, and the practical application of hybrid quantum algorithms to solve complex problems.
Publications
2026 |
D. Martín-Pérez and F. Rodríguez-Díaz and A. R. Troncoso-García and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez Rule-Based Explainability for Quantum Machine Learning in Thyroid Cancer Prediction Conference CAEPIA 21st Conference of the Spanish Association for Artificial Intelligence, Lecture Notes in Artificial Intelligence 2026. @conference{CAEPIA26_Martin,This paper presents an exploratory study on the use of rule-based explainability to support clinical decision-making in thyroid cancer recurrence prediction. Quantum machine learning models are compared within a unified experimental workflow, and a model-agnostic explainability technique is applied to extract interpretable rules. These rules are consistent with established clinical risk factors and are further used for explainability-driven feature selection, leading to improved model behavior, particularly in terms of sensitivity. The results provide useful information on the applicability of explainable quantum models and show consistent behavior in both ideal and noisy computational environments, highlighting their potential for interpretable decision support in healthcare. |
D. Martín-Pérez and F. Rodríguez-Díaz and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez Hybrid Classical-Quantum Transfer Learning with Noisy Quantum Circuits Journal Article In: Computer Modeling in Engineering and Sciences, vol. 147, pp. 82712, 2026. @article{MARTIN26_CMES,Hybrid classical-quantum architectures have emerged as a practical response to the data and compute demands of modern deep learning, since a pretrained classical backbone can carry the feature extraction while a compact quantum head provides the trainable component. Quantum transfer learning is the most active instance of this idea. However, existing quantum transfer learning pipelines have been evaluated in isolation, typically on a single software framework and without a structured treatment of noise or statistical significance, which makes it difficult to assess how this paradigm contributes over fair classical baselines. A methodological benchmark for quantum transfer learning is proposed in this paper. The benchmark couples a common set of frozen pretrained convolutional backbones to several classical and quantum classification heads, implemented in both PennyLane and Qiskit, so that the contribution of nonlinearity, software framework, and quantum component can be analyzed separately. The benchmark has been applied to heterogeneous image datasets covering medical, biological, industrial, and general-vision domains, and most configurations have been executed in three environments: ideal simulation, noisy emulation calibrated on IBM Heron r2 and real IBM quantum hardware. Complementary analyses isolate the contribution of individual noise channels, examine scalability with qubit count and circuit depth and assess the presence of barren plateaus. Statistical reliability is ensured through multiple random seeds and pairwise Wilcoxon signed-rank tests, providing the first controlled cross-framework assessment of quantum transfer learning under calibrated noise and on real hardware. |
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. |
2025 |
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. |