Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware
- Johannes Weidenfeller
- Lucia Cuervo Valor
- et al.
- 2022
- arXiv
Bio
Dr. Daniel J. Egger is a researcher in the Quantum Applications Research & Software group in IBM Research – Zurich. His current research focuses on quantum information processing with superconducting circuits, pulse-level control of quantum systems, and quantum algorithms.
He was awarded a PhD with distinction from the Saarland University (Germany) in 2014 for his thesis entitled “Optimal control and quantum simulations in superconducting quantum devices”. For his PhD thesis, he received the Dr. Eduard-Martin prize.
Daniel J. Egger holds an MSc degree from EPFL Lausanne Switzerland.
Publications
High-Speed Calibration and Characterization of Superconducting Quantum Processors without Qubit Reset
- Max Werninghaus
- Daniel Egger
- et al.
- 2021
- PRX Quantum
Warm-starting quantum optimization
- Daniel Egger
- Jakub Marecek
- et al.
- 2021
- APS March Meeting 2021
High speed calibration and characterization of superconducting qubits without qubit reset
- Max Werninghaus
- Daniel Egger
- et al.
- 2021
- APS March Meeting 2021
Control, Calibration and Characterization of a simulated two-transmon chip. Part 1
- Federico Roy
- Nicolas Wittler
- et al.
- 2021
- APS March Meeting 2021
Control, Calibration and Characterization of a simulated two-transmon chip. Part 2
- Nicolas Wittler
- Federico Roy
- et al.
- 2021
- APS March Meeting 2021
Machine-learning tools for rapid control, calibration and characterization of QPUs and other quantum devices
- Nicolas Wittler
- Federico Roy
- et al.
- 2021
- APS March Meeting 2021
Quantum Algorithms for Mixed Binary Optimization Applied to Transaction Settlement
- Lee Braine
- Daniel Egger
- et al.
- 2021
- IEEE TQE
Quantum Computing for Finance: State-of-the-Art and Future Prospects
- Daniel Egger
- Claudio Gambella
- et al.
- 2020
- IEEE TQE
Benchmarking the noise sensitivity of different parametric two-qubit gates in a single superconducting quantum computing platform
- Marc Ganzhorn
- Gian Von Salis
- et al.
- 2020
- PRResearch
Patents
- AU
- 2021379051
- US
- 11941490
- US
- 11928004
- CN
- ZL201980020536.0
- FR
- 3769271
- GB
- 3769271
- DE
- 602019043546.0
- US
- 11809964
- US
- 11651264
- US
- 11562281
Projects
Quantum Optimization
Applying quantum algorithms to various optimization problems
Top collaborators
SW
Stefan Woerner
Manager, Quantum Computational Science
JG
Julien Gacon
Research Scientist, Quantum Computational Science
IT
Ivano Tavernelli
Global leader for advanced algorithms for quantum simulations
MW
Mira Luca Wolf-Bauwens
Responsible Quantum Computing Lead