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
- Near-term quantum algorithms
- Quantum machine learning
- Generative modeling
- High-performance numerical simulations
Biography
Manuel Rudolph is a Ph.D. student at EPFL in Prof. Zoë Holmes' recently formed group. He works on quantum algorithms for near- to mid-term quantum devices, focusing on numerical methods for quantum machine learning approaches such as generative modeling and process learning. 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 20223 - 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
Publications
Selected publications
| 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, Jacob Miller, Jing Chen, Atithi Acharya, and Alejandro Perdomo-Ortiz |
Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage |
| 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 |
| Manuel S. Rudolph, Jing Chen, Jacob Miller, Atithi Acharya, and Alejandro Perdomo-Ortiz |
Decomposition of Matrix Product States into Shallow Quantum Circuits |
| Noah L. Wach, Manuel S. Rudolph, Fred Jendrzejewski, and Sebastian Schmitt Quantum Machine Intelligence |
Data re-uploading with a single qudit |
| Sofiene Jerbi, Joe Gibbs, Manuel S. Rudolph, Matthias C. Caro, Patrick J. Coles, Hsin-Yuan Huang, and Zoë Holmes |
The power and limitations of learning quantum dynamics incoherently |
| Atithi Acharya, Manuel S. Rudolph, Jing Chen, Jacob Miller, and Alejandro Perdomo-Ortiz |
Qubit seriation: Improving data-model alignment using spectral ordering |
Teaching & PhD
Intro to generative models on quantum hardware
4-part introductory course on quantum generative modelling with step-by-step code examples.Link