LAIA DOMINGO

Research Fellow at QML-CVC

About me

I am a Research Fellow in the QML-CVC group, specializing in quantum machine learning and hybrid quantum-classical algorithms. I hold a PhD in quantum machine learning from the Universidad Politécnica de Madrid (UPM), and my work focuses on areas such as quantum reservoir computing, quantum extreme learning machines, quantum optimization, and practical applications of quantum AI.

I have also worked as Chief Science Officer at Ingenii, a quantum machine learning startup, where I contributed to the development of quantum technologies for real-world use cases in life sciences. Alongside my research, I am active in scientific communication and education, with a strong interest in making quantum computing more practical, understandable, and impactful.

Publications

  • Dana Kosaybati, Adam Yahya, Mohammad Chalhoub, Mahdi Chehimi, Laia Domingo, and Samuel Yen-Chi Chen. Tail-Weighted Quantum Generative Adversarial Networks for Learning Heavy-Tailed Distributions. IEEE Quantum Week (CQE, 2026).
  • Mohammad Chalhoub, Mahdi Chehimi, Laia Domingo, Omar Alhussein, Ahmed Farouk, and Saif Al-Kuwari. Superpixel-Based QUBO for Scalable Quantum-Enhanced Medical Image Segmentation. IEEE Quantum Week (CQE, 2026)
  • Domingo, L., Chehimi, M. Quantum-enhanced unsupervised image segmentation for medical image analysis. npj Quantum Information (in review), 2025.
  • Domingo, L., Johnson, C. Quantum-enhanced optimization for patient stratification in clinical trials. 2025 IEEE International Workshop on Quantum Computing, 2025.
  • Domingo, L., Grande, M., Borondo, F., Borondo, J. Quantifying the uncertainty of reservoir computing: Confidence intervals for time-series forecasting. Mathematics, 2024, 12(19): 3078.
  • Domingo, L., Carlo, G., Borondo, F., Wisniacki, D., Roncaglia, A., Scialchi, G. Quantum reservoir complexity by the Krylov evolution approach. Physical Review A, 2024.
  • Domingo, L., Borondo, J., Borondo, F. Using reservoir computing to construct scarred wave functions. Physical Review E, 2024, 109(4): 044214.
  • Domingo, L., Grande, M., Carlo, G., Borondo, F., Borondo, J. Optimal quantum reservoir computing for market forecasting: An application to fight food price crises. Machine Learning: Science and Technology, 2024.
  • Domingo, L., Chehimi, M., Banerjee, S., He, Y. S., Konakanchi, S., Ogunfowora, L., Roy, S., Selvarajan, S., Djukic, M., Johnson, C. A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery. 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 2024, 2: 126–131.
  • Domingo, L., Djukic, M., Johnson, C., Borondo, F. Binding affinity predictions with hybrid quantum-classical convolutional neural networks. Scientific Reports, 2023, 13(1): 17951.
  • Domingo, L., Carlo, G., Borondo, F. Taking advantage of noise in quantum reservoir computing. Scientific Reports, 2023, 13(1): 8790.
  • Domingo, L., Grande, M., Borondo, F., Borondo, J. Anticipating food price crises by reservoir computing. Chaos, Solitons & Fractals, 2023, 174: 113854.
  • Domingo, L., Carlo, G., Borondo, F. Optimal quantum reservoir computing for the noisy intermediate-scale quantum era. Physical Review E, 2022, 106(4): L043301.
  • Domingo, L., Borondo, J., Borondo, F. Adapting reservoir computing to solve the Schrödinger equation. Chaos, 2022, 32(6): 063111.
  • Domingo, L., Borondo, F. Deep learning methods for the computation of vibrational wavefunctions. Communications in Nonlinear Science and Numerical Simulation, 2021, 103: 105989.
  • Domingo, L., Skotiniotis, M., Muñoz-Tapia, R. Reinforcement learning for optimal error correction of toric codes. Physics Letters A, 2020, 384(17): 126353.
  • Domingo, L., Hoban, M., Šupić, I., Acín, A. Self-testing and certification using trusted quantum inputs. New Journal of Physics, 2020, 22(7): 073006.

Ph.D. Thesis

PhD Title: Classical and Quantum Reservoir Computing: Development and Applications in Machine Learning
Supervisors: Prof. Florentino Borondo, Javier Borondo
Doctoral Program: Complex systems
Institution: Universidad Politécnica de Madrid
Year of Completion: 2023

Software

Software contributions form an integral part of my research. I have authored several GitHub repositories supporting my work, including implementations of classical and quantum reservoir computing and their applications across different domains.

I am also the designer of ingenii-quantum, a Python library for quantum and quantum-inspired machine learning.

GitHub: https://github.com/laiadc

Conference contributions

  • Summer School: Machine Learning in Quantum Physics and Chemistry, University of Warsaw, Poland (24 August – 3 September 2021). Poster presentation: “Deep learning methods for the computation of vibrational wavefunctions.”
  • Quantum Matter 2022. Poster presentation: “Optimal quantum reservoir computing for the NISQ era.”
  • NoLineal 20-21. Poster presentation: “Deep learning methods for the computation of vibrational wavefunctions.”
  • IEEE International Conference on Quantum Computing and Engineering (QCE), 2024. Talk: “A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery.”
  • BioIT 2024. Talk: “Quantum machine learning applications for life sciences.”
  • Digital Enterprise Show, 2026. Talk: “Quantum computing: breaking tech limits.”
  • European Summer School on Quantum AI, 2026. Talk: “Quantum Reservoir Computing and Quantum Extreme Learning Machines: Theory, Design, and Experiments.”

    Contact information

    E-mail: ldomingo@cvc.uab.cat

    LinkedIn: https://www.linkedin.com/in/laia_domingo

    Scholar: https://scholar.google.com/citations?user=z98sXO8AAAAJ&hl=es