Experience

1. Lead Software Developer

   Pasqal June 2024 – Present
   • Architected and maintained open-source and internal Quantum and Machine Learning libraries enabling scalable simulations, integration for research and industry applications, increasing traffic and potential leads for the company by at least 50%.
   • Delivered significant performance gains, with runtime improvements ranging from 50% to 1000% faster.
   • Applications for customer use cases: Physics-Informed (quantum) Neural Networks for solving Partial Differential Equations, Machine Learning for Chemistry and Optimization problems.
   • Open-source Python librairies produced within the Agile Software team:
     • For building digital-analog programs in Pytorch or JAX: Qadence, QoolQit, PyQTorch, Horqrux, Quantum error models package), Qadence-protocols (Mitigation and measurement protocols).
     • Machine Learning and optimization: Quantum Evolution Kernel (Quantum feature maps for graph machine learning), Perceptrain (Distributed Machine learning), Maximum independent set.

2. Research Scientist

   Stealth Startup September 2023 – February 2024
   • Engineered specialized software solutions for next-generation physics-based AI hardware.
   • Developed and optimized JAX-based libraries integrating generative models and benchmarking frameworks.

3. Quantum Computing Fellow

   Los Alamos National Laboratory June 2022 – August 2022
   • Selected as a fellow for the highly competitive Quantum Computing Summer School, recognized in quantum technologies.
   • Implemented Quantum Machine Learning algorithms on simulators and real quantum hardware to process quantum-native data.

4. Scientific Consultant

   Modis - TotalEnergies March 2019 – June 2019
   • Scientific advisor for TotalEnergies in their machine learning and quantum computing project.
   • Implemented algorithms on high-performance clusters (CPU + GPU).

5. Researcher

   Oak Ridge National Laboratory - TotalEnergies August 2017 – January 2019
   • Investigated and highlighted quantum computing and applications potential for industrial use cases in the energy sector in Machine Learning, Chemistry, Optimization, and Differential equations.

6. Data Scientist

   Sarenza (Leader in selling shoes online in France) April 2016 – October 2016
   • Designed and implemented daily-updated fact tables using Hive (SQL for Hadoop), significantly reducing data preparation time for Data Science workflows.
   • Built a recommendation system using collaborative filtering techniques to enhance user personalization and engagement.
   • Applied transfer learning for feature extraction to improve the quality and performance of clustering models.
   • Developed scalable sales forecasting solutions using machine learning algorithms (Random Forests, XGBoost, etc.) in Python and Spark, leading to more accurate business planning and inventory management.

Education

1. PhD in Quantum Machine Learning

   Leiden University June 2019 – August 2023
   • Completed industry-sponsored Ph.D. research (TotalEnergies) developing quantum-classical hybrid solutions for real-world optimization and machine learning challenges, bridging academic quantum computing advances with industrial energy sector applications.
   • Authored doctoral thesis advancing NISQ algorithm selection and configuration methodologies, establishing frameworks for deploying quantum algorithms in production industrial environments with hardware constraints.
   • Designed and implemented multiple quantum machine learning algorithms (QAOA, VQE, Quantum Neural Networks, quantum generative models) with custom variational optimizers, creating research tools adopted by academic and industrial institutions.
   • Applied quantum-inspired generative models for in-house datasets of small molecules (antioxidants) from TotalEnergies.
   • Delivered technical presentations and developed comprehensive tutorials for quantum computing education while mentoring graduate students in quantum algorithm development and implementation.
   Read Thesis

2. Master’s Degree in Mathematical Engineering

   National Institute of Applied Sciences (School of Engineering), Rouen September 2011 – May 2016
   • Applied Mathematics (Statistics, Optimization, Machine Learning, Partial Differential Equations).
   • Computer Science (Programming, Virtual reality, Web Technologies).}

3. Master's degree in Actuaries and Mathematical Engineering in Insurance and Finance

   University of Rouen September 2015 – May 2016
   • Insurance, Finance, Economy, Management, Banking and Finance Law.
   • Mathematics (Pricing, NonParametric Tests, Statistics of extreme values, Survival Analysis, Risk Management).

Skills & Hobbies

Technical Skills

Python

Jax

Data Science

SQL

Quantum

Hobbies

Hiking

Traveling

Coding

Awards

Second place in 2022 QHack coding challenges. First place in two open coding challenges and second prize in two others.

Xanadu ∙ February 2022

In this Qhack 2022 Open Hackathon, we proposed to re-implement concepts related to asynchronous Quantum Computing, also framed as Ensemble Quantum Computing, for Variational Quantum Algorithms (http://arxiv.org/abs/2111.14940). Rather than relying on just one physical device (or backend), we run algorithms on several machines/backends, which can speed up runtime of algorithms. VQE and QAOA have been targeted, but this can be applied to other algorithms and applications such as Quantum Machine Learning, Finance, etc.

Second place at the BIG Quantum Hackathon by QuantX, Paris 2021

QuantX ∙ September 2021

Implemented a Wasserstein quantum GAN with Gradient Penalty and applied to images provided by BMW for car design.

Languages

100%

French

100%

English

50%

Spanish

25%

Dutch

10%

Japanese