UNIVERSIDAD PABLO DE OLAVIDE
Publications
2025
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, 2025.
Links | BibTeX | Tags: quantum computing
@article{CSUR2025,
title = {A Survey of Quantum Machine Learning: Foundations, Algorithms, Frameworks, Data and Applications},
author = {F. Rodríguez-Díaz and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},
url = {https://dl.acm.org/doi/10.1145/3764582},
doi = {10.1145/3764582},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
journal = {ACM Computing Surveys},
volume = {58},
issue = {4},
pages = {1-35},
keywords = {quantum computing},
pubstate = {published},
tppubtype = {article}
}
2024
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.
Abstract | Links | BibTeX | Tags: quantum computing
@conference{RODRIGUEZ-DIAZ24b,
title = {An Experimental Comparison of Qiskit and Pennylane for Hybrid Quantum-Classical Support Vector Machines},
author = {F. Rodríguez-Díaz and J. F. Torres and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},
url = {https://link.springer.com/chapter/10.1007/978-3-031-62799-6_13},
doi = {https://doi.org/10.1007/978-3-031-62799-6_13},
year = {2024},
date = {2024-06-06},
booktitle = {CAEPIA Conference of the Spanish Association for Artificial Intelligence},
pages = {121-130},
series = {Lecture Notes in Artificial Intelligence},
abstract = {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.},
keywords = {quantum computing},
pubstate = {published},
tppubtype = {conference}
}
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.