Image

Feld Group

Image

Sebastian Feld

Associate Professor | Group Leader, Feld Group
Full-Stack Quantum Computing
Quantum Computing Division

S.Feld@tudelft.nl
Mekelweg 4, HB 11.060
Management Assistant: Julijana Avramov

Research

Sebastian Feld is an associate professor at Delft University of Technology and leads the Feld Group at QuTech. His research focuses on full-stack quantum computing: how quantum computers should be designed, programmed, and operated so that promising algorithms can become reliable computations on real hardware.

The Feld Group works across the quantum computing stack, connecting quantum computer architecture with compilation, system software, and applications. On the systems side, the group develops scalable and modular quantum architectures, hardware-aware compilation methods, and techniques for reliable quantum computation. On the application side, it develops quantum algorithms for realistic optimization, planning, and engineering problems.

A central theme of this work is hardware-software co-design: rather than considering quantum hardware and applications separately, the group studies how decisions across the stack influence each other and how they can be optimized together. The long-term goal is to help turn increasingly capable quantum processors into useful computing systems.

Research areas

Quantum computer architecture · Quantum compilation & systems · Reliable quantum computing · Quantum algorithms & applications

Background

Before joining TU Delft and QuTech, Sebastian led the Quantum Applications and Research Laboratory (QAR-Lab) at LMU Munich, where he worked on quantum-assisted artificial intelligence and optimization. He received his doctorate from LMU Munich for research in time series analysis.

Feld Group · Personal website

Get a glimpse of QuTech!

Are you curious about QuTech, our labs where we work on the development of quantum internet and quantum computers or our awesome colleagues? Watch this video and get a glimpse of QuTech!

Cookie policy

This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful.

Strictly Necessary Cookies

Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings.

Analytics

This website uses Google Analytics to collect anonymous information such as the number of visitors to the site, and the most popular pages.

Keeping this cookie enabled helps us to improve our website.