Manuel Sebastian Rudolph

Expertise Computational quantum physicsQuantum algorithmsQuantum machine learningGenerative modeling Manuel is a PhD Candidate at EPFL in the group of Prof. Zoë Holmes, where he focuses on the numerical simulation of quantum computers. Together with his collaborators, he studies which properties make quantum circuits easy or hard to simulate classically, and how efficient classical simulability is connected to the barren plateau phenomenon. Manuel develops high-performance quantum simulation software and is the main developer of the "PauliPropagation.jl" library. He was awarded the 2024 Google PhD Fellowship in quantum computing and was a visiting student researcher at the Los Alamos National Laboratory, the Flatiron Institute New York, and Google Quantum AI in Santa Barbara. Before the start of his PhD, Manuel worked as a Quantum Application Scientist at Zapata Computing, where he developed hybrid algorithms utilizing classical and quantum computing resources. Education PhD in Physics | 2022 – 2026 EPFL MSc in Physics | 2017 – 2020 University of Heidelberg BSc in Physics | 2014 – 2017 University of Heidelberg Professionals experiences Student Researcher 2025–2026 Google Quantum AIPre-Doctoral Researcher 2025–2025 CCQ Flatiron InstituteQuantum Research Fellow 2023–2023 Los Alamos National LabQuantum Application Scientist 2021–2022 Zapata ComputingQuantum Application Intern 2020–2020 Zapata Computing Teaching & PhDIntro to generative models on quantum hardware 4-part introductory course on quantum generative modelling with step-by-step code examples.LinkTeaching Assistant Quantum Information Theory, Quantum Physics 2, Thermodynamics Awards PASQAL [re]Generative Challenge Lead of the second place project among 70 teams in the Hackathon. Total prize money 11.000€ 2023 Google PhD Fellowship in Quantum Computing Selected as one of 3 PhD students world-wide to receive $80,000 funding and a mentor from Google Quantum AI. 2024