Charles Moussa ☕️
Charles Moussa

Lead Software Developer

Pasqal

About Me

Hi, there! I’m Charles, a Mathematical Engineer/Research Scientist currently living in the Netherlands, where I obtained my PhD from Leiden University. I like researching about topics such as artificial intelligence, quantum computing, and accelerators in a high-performance computing stack. When investigating cutting-edge technologies, my main interest is to understand their potential applications, design new modules or pipelines to improve an existing workflow, analyze and understand performances, and combine multiple fields to benefit applications. I also appreciate developing proof-of-concepts or prototypes for customers’ use cases.

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Interests
  • Artificial Intelligence
  • Quantum computing
  • HPC/Accelerators
  • Optimization
Education

  • PhD in Quantum Machine Learning

    Leiden University

  • Master’s Degree in Mathematical Engineering

    National Institute of Applied Sciences (School of Engineering), Rouen

  • Master's degree in Actuaries and Mathematical Engineering in Insurance and Finance

    University of Rouen

📚 My Research

I like researching about topics such as artificial intelligence, quantum computing, and accelerators in a high-performance computing stack. When investigating cutting-edge technologies, my main interest is to understand their potential applications, design new modules or pipelines to improve an existing workflow, analyze and understand performances, and combine multiple fields to benefit applications.

I also appreciate developing software, proof-of-concepts or prototypes for customers’ use cases.

Please reach out to collaborate 😃

Featured Publications
Application of quantum-inspired generative models to small molecular datasets
Hyperparameter importance and optimization of quantum neural networks across small datasets
Resource frugal optimizer for quantum machine learning
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Recent Publications
Charles Moussa, Hao Wang, Mauricio Araya-Polo, Thomas Bäck, Vedran Dunjko (2023). Application of quantum-inspired generative models to small molecular datasets. C. Moussa, H. Wang, M. Araya-Polo, T. Bäck and V. Dunjko, “Application of quantum-inspired generative models to small molecular datasets,” 2023 IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA, USA, 2023, pp. 342-348..
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Charles Moussa, Yash J. Patel, Vedran Dunjko, Thomas Bäck and Jan N. Van Rijn (2023). Hyperparameter importance and optimization of quantum neural networks across small datasets. Moussa, C., Patel, Y.J., Dunjko, V. et al. Hyperparameter importance and optimization of quantum neural networks across small datasets. Mach Learn 113, 1941–1966 (2024)..
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Charles Moussa, Max Hunter Gordon, Michal Baczyk, M Cerezo, Lukasz Cincio and Patrick J Coles (2023). Resource frugal optimizer for quantum machine learning. Charles Moussa et al 2023 Quantum Sci. Technol. 8 045019.
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Xavier Bonet-Monroig, Hao Wang, Diederick Vermetten, Bruno Senjean, Charles Moussa, Thomas Bäck, Vedran Dunjko, Thomas E O'Brien (2023). Performance comparison of optimization methods on variational quantum algorithms. Phys. Rev. A 107, 032407.
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Charles Moussa, Hao Wang, Thomas Bäck and Vedran Dunjko (2022). Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm. EPJ Quantum Technol. (2022) 9: 11.
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