Classical Limits of Spectral Filtering in Quantum Generative Models
M. Roth
Introduction
I am a researcher at the intersection of quantum computing and artificial intelligence. My research interests span from the application of machine learning techniques over quantum algorithms to quantum-based data processing for applications such as optimization and simulation. I currently head the Fraunhofer Research and Innovation Center Applied Quantum Artificial Intelligence in Heilbronn and co-lead flaQship — the Fraunhofer Lab for Applied Quantum Computing in Stuttgart-Heilbronn.
Career
Head of Quantum Machine Learning
Fraunhofer IAO, Heilbronn
2026 —
Group Lead Quantum Computing
Fraunhofer IPA, Stuttgart
2022 — 2026
Quantum Computing Scientist
Fraunhofer IPA, Stuttgart
2021 — 2022
Data Scientist
MAHLE GmbH, Stuttgart
2019 — 2021
PhD Quantum Information Theory
RWTH Aachen University, FZ Jülich, Aachen
2016 — 2019
M.Sc. Physics
ETH Zürich, Heidelberg University, FZ Jülich, Aachen
2014 — 2016
B.Sc. Physics
Heidelberg University, Heidelberg
2011 — 2014
Selected Works
Grokking and epoch-wise double descent in quantum neural networks
D. Pranjić, M. Roth, C. Tutschku
Probabilistic Ranking for Transfer Learning Bayesian Optimization
P. Wagner, H. Amirkhanian, M. Roth, M. F. Huber
Exponential Scaling Barriers for Variational Quantum Eigensolvers
M. Hagelüken, D. A. Kreplin, F. Wieland, M. F. Huber, M. Roth
Quantum Deep Learning: A Comprehensive Review
Y. Ji, Z.-Y. Chen, M. Roth, D. A. Kreplin, C. Schiffer, M. King, O. Anton, M. S. Alam, M. Krutzik, D. Willsch, L. Mathey, F. K. Wilhelm, G.-P. Guo
Data-Efficient Quantum Noise Modeling via Machine Learning
Y. Ji, M. Roth, D. A. Kreplin, I. Polian, F. K. Wilhelm
Data Efficient Prediction of excited-state properties using Quantum Neural Networks
M. Hagelüken, M. F. Huber, M. Roth
Quantum Kernel Methods under Scrutiny: A Benchmarking Study
J. Schnabel, M. Roth
On the similarity of bandwidth-tuned quantum kernels and classical kernels
R. F. Ablan, M. Roth, J. Schnabel
Reinforcement learning-based architecture search for quantum machine learning
F. Rapp, D. A. Kreplin, M. Roth
AutoQML: A Framework for Automated Quantum Machine Learning
M. Roth, D. A. Kreplin, D. Basilewitsch, et al.
sQUlearn: A Python Library for Quantum Machine Learning
D. A. Kreplin, M. Willmann, J. Schnabel, F. Rapp, M. Hagelüken, M. Roth
Application of quantum annealing for scalable robotic assembly line optimization: a case study
M. Willmann, M. Albus, J. Schnabel, M. Roth
Reduction of finite sampling noise in quantum neural networks
D. A. Kreplin, M. Roth
A heuristic for solving the irregular strip packing problem with quantum optimization
P. A. Matt, M. Roth
Quantum Gaussian Process Regression for Bayesian Optimization
F. Rapp, M. Roth
A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules
P. A. Matt, R. Ziegler, D. Brajovic, M. Roth, M. F. Huber
Time-resolved tomography of a driven adiabatic quantum simulation
G. Salis, N. Moll, M. Roth, M. Ganzhorn, S. Filipp
Optimal gauge for the multimode Rabi model in circuit QED
M. Roth, F. Hassler, D. P. DiVincenzo
Gate-efficient simulation of molecular eigenstates on a quantum computer
M. Ganzhorn, ..., M. Roth, et al.
Adiabatic quantum simulations with driven superconducting qubits
M. Roth, N. Moll, G. Salis, M. Ganzhorn, D. J. Egger, S. Filipp, S. Schmidt
Analysis of a parametrically driven exchange-type gate and a two-photon excitation gate between superconducting qubits
M. Roth, M. Ganzhorn, N. Moll, S. Filipp, G. Salis, S. Schmidt
Full list on Google Scholar.
Contact
Interested in a master thesis or open positions? Reach out to me on LinkedIn.