Manuel Rudolph
+41 21 693 89 02
EPFL
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QIC
Web site: Web site: https://www.epfl.ch/labs/qic/
Fields of expertise
Quantum machine learning
Computational quantum physics
Generative modeling
Biography
Manuel Rudolph is a Ph.D. student at EPFL in the group of Prof. Zoë Holmes. He works on quantum algorithms for near- to mid-term quantum devices, focusing on numerical methods for simulating quantum systems and quantum machine learning approaches. Manuel is the main developer of the LOWESA algorithm -- a classical surrogate algorithm for simulating expectation values -- and pushes Pauli propagation methods in general. Previously, he worked as a Quantum Application Scientist at Zapata Computing, where he developed hybrid algorithms utilizing classical and quantum computing resources. Among other software projects, he also developed the Python visualization library orqviz.Professional course
Quantum Research Fellow
Summer fellowship
Los Alamos National Lab
June 2023 - August 2023
Quantum Application Scientist
Full-time position
Zapata Computing
January 2021 - September 2022
Quantum Application Intern
Post-Master's internship
Zapata Computing
June 2020 - December 2020
Education
Ph.D.
Physics
EPFL
September 2022 - ongoing
Master's
Physics
University of Heidelberg
October 2017 - March 2020
Bachelor's
Physics
University of Heidelberg
October 2014 - September 2017
Awards
2023 : PASQAL [re]Generative Challenge : Lead of the second place project among 70 teams in the Hackathon. Total prize money 11.000€. "NeutroGen: Unlocking Data-Driven Applications for Neutral Atoms"
Publications
Selected publications
| Manuel S. Rudolph, Enrico Fontana, Lukasz Cincio, Zoë Holmes |
Classical surrogate simulation of quantum systems with LOWESA |
| M. Cerezo, Martin Larocca, Diego García-Martín, N. L. Diaz, Paolo Braccia, Enrico Fontana, Manuel S. Rudolph, Pablo Bermejo, Aroosa Ijaz, Supanut Thanasilp, Eric R. Anschuetz, and Zoë Holmes |
Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing |
| Manuel S. Rudolph, Jacob Miller, Jing Chen, Atithi Acharya, and Alejandro Perdomo-Ortiz Nature Communications |
Synergistic pretraining of parametrized quantum circuits via tensor networks |
| Manuel S. Rudolph*, Sacha Lerch*, Supanut Thanasilp*, Oriel Kiss, Sofia Vallecorsa, Michele Grossi, and Zoë Holmes |
Trainability barriers and opportunities in quantum generative modeling |
| Enrico Fontana, Manuel S. Rudolph, Ross Duncan, Ivan Rungger, and Cristina Cîrstoiu |
Classical simulations of noisy variational quantum circuits |
| Manuel S. Rudolph, Jing Chen, Jacob Miller, Atithi Acharya, and Alejandro Perdomo-Ortiz Quantum Science and Technology (QST) |
Decomposition of Matrix Product States into Shallow Quantum Circuits |
| Manuel S. Rudolph, Ntwali Bashige Toussaint, Amara Katabarwa, Sonika Johri, Borja Peropadre, and Alejandro Perdomo-Ortiz Physical Review X (PRX) |
Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer |
| Armando Angrisani, Alexander Schmidhuber, Manuel S. Rudolph, M. Cerezo, Zoë Holmes, Hsin-Yuan Huang |
Classically estimating observables of noiseless quantum circuits |
Teaching & PhD
Intro to generative models on quantum hardware
4-part introductory course on quantum generative modelling with step-by-step code examples.Link