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<!DOCTYPE html><html lang="en-us" >
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<h2>Charles Moussa</h2>
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<p style="font-size: 1.2rem; background: #FFB76B; background: linear-gradient(to right, #FFB76B 0%, #FFA73D 30%, #FF7C00 60%, #FF7F04 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">👋 Hi, there! I’m <strong>Charles</strong>, 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.</p>
<p>Check out my <a href="/about/">resumé</a>, social media above, publications on Google Scholar, and GitHub portfolio below! Don’t hesitate to get in touch!</p>
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<a href="https://github.com/chMoussa/adaptative_vqa_optimizers" target="_blank" rel="noopener">Adaptative VQA Optimizers</a>
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<a href="https://github.com/chMoussa/adaptative_vqa_optimizers" target="_blank" rel="noopener" class="summary-link">
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During QHack 2023, we implemented with Pennylane a resource frugal optimizer named Refoqus from the paper <a href="https://doi.org/10.1088/2058-9565/acef55" target="_blank" rel="noopener">Resource frugal optimizer for quantum machine learning</a>. We show how to apply it across 3 QML applications on quantum datasets.
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alt="Adaptative VQA Optimizers" loading="lazy">
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