IBM Quantum Job Scheduling
Designed and implemented the fair-share queue that decides the order in which jobs run on IBM Quantum hardware. It balances access across users and organizations according to their allocations, and it schedules every job run on every IBM Quantum QPU — still in production today.
Later, as Data & Intelligence Chapter Lead, I led work on predicting QPU processing time for user jobs, which has been deployed on production since 2023. Based on this experience, we published a paper at IEEE QCE 2025 in which we used a dataset of ~150,000 jobs following the IBM Quantum schema, gradient-boosting models (LightGBM) forecast how long each job will take on the hardware, giving scheduling and resource management a better signal than static estimates.
I research and develop AI-based systems to tackle complex problems in quantum computing and other relevant areas.
My work sits at the intersection of quantum computing, machine learning, and open source. I hold IBM Master Inventor status and have contributed to tools used by millions of developers worldwide.
I have a PhD in Computer Engineering from the University of Salamanca (2018), have authored 80+ publications, and received the 2019 SCIE-BBVA Award for best young researcher in Computer Science in Spain.