WP4: Quantum-Machine Learning approach
This WP investigates the applicability of quantum machine learning (QML) to biomedical signal processing, trying to use the quantum properties to obtain a better understanding of complex correlations in high-dimensional data. This process will be carried out in parallel with the previous WPs dedicated to next-generation ML algorithms, searching for synergies between them.
QML applicability and data encoding
Investigate QML for biomedical signals and encode data into a quantum system.
Quantum feature map construction
Construct a quantum feature map that's hard to simulate classically for potential quantum advantage.
QNNs (Ansatz) or Quantum Kernels
Develop either parameterized quantum circuits (Ansatz) for quantum neural networks or use quantum kernels for kernel methods.
Attention layers
Analyze introducing attention layers to quantum models to move towards Transformer-like architectures.
Publications
2025
Performance Analysis of Quantum Support Vector Classifiers and Quantum Neural Networks
[Conference] International Joint Conference on Neural Networks 2025
Villalba-Ferreiro, Tomás; Mosqueira-Rey, Eduardo; Alvarez-Estevez, Diego
This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. We find that quantum models tend to outperform classical approaches as the problem complexity increases, as QNNs exhibit superior performance in higher-complexity tasks due to their increased quantum load. These findings highlight the potential of Quantum Machine Learning (QML) for complex classification problems and provide insights into model selection and optimization strategies.
Unified Framework for Implementing Inaccurate Knowledge in Quantum Symbolic Artificial Intelligence Models
[Conference] 17th International Conference on Agents and Artificial Intelligence
Mosqueira-Rey, Eduardo; Magaz-Romero, Samuel; Moret-Bonillo Vicente
We present different models for implementing inaccurate knowledge in quantum computers and propose a unified framework to represent and implement the common features of all of them.
Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks
[Journal] International Joint Conference on Neural Networks 2025
Alvarez-Estevez, Diego
Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods remains an open question, particularly beyond the context of specifically crafted toy problems, and given the current limitations of quantum hardware. This study focuses on quantum kernel methods in the context of classification tasks. In particular, it examines the performance of quantum kernel estimation and quantum kernel training (QKT) in connection with two quantum feature mappings, namely, ZZFeatureMap and CovariantFeatureMap. Remarkably, these feature maps have been proposed in the literature under the conjecture of possible near-term quantum advantage and have shown promising performance in ad hoc datasets. This study aims to evaluate their versatility and generalization capabilities in a more general benchmark, encompassing both artificial and established reference datasets. Classical machine learning methods, specifically support vector machines and logistic regression, are also incorporated as baseline comparisons. Experimental results indicate that quantum methods exhibit varying performance across different datasets. Despite outperforming classical methods in ad hoc datasets, mixed results are obtained for the general case among standard classical benchmarks. The experimental data call into question a general added value of applying QKT optimization, for which the additional computational cost does not necessarily translate into improved classification performance. Instead, it is suggested that a careful choice of the quantum feature map in connection with proper hyperparameterization may prove more effective.