Dr. Simon Hofmann

Postdoc

Arcisstrasse 21, 3rd floor, room 3946
Phone: +49 (89) 289 - 23556
simon.t.hofmann@tum.de

Simon Hofmann is a postdoctoral researcher at the Chair for Design Automation at the Technical University of Munich (TUM). His research spans quantum compiler infrastructure based on MLIR/LLVM and physical design for field-coupled nanocomputing, with an emphasis on machine learning and optimization. He joined the chair in December 2022 and received his Dr.-Ing. in Computer Science from TUM in January 2026, graduating summa cum laude.

Research

Quantum Compilation with MLIR and LLVM

Simon contributes to the MQT Compiler Collection in MQT Core, developing compilation flows for quantum programs with classical control flow using MLIR and LLVM. His work includes optimization passes for single- and multi-qubit gates, gate decomposition and synthesis, and compilation that accounts for the constraints of quantum hardware.

Alongside his academic work, he is a Senior Quantum Software Engineer at the Munich Quantum Software Company (MQSC), where he develops software for quantum computing.

Physical Design for Field-Coupled Nanocomputing

Field-coupled nanocomputing explores circuit technologies that encode and transmit information through interactions between nanoscale devices. Simon develops algorithms and open-source tools that translate logic circuits into physical layouts, helping make these emerging technologies easier to design, evaluate, and use.

Within nanocomputing, his research interests include:

Placement and routing: generating circuit layouts under the geometric and clocking constraints of field-coupled nanotechnologies.
Machine learning for design automation: using deep reinforcement learning to explore placement and routing decisions.
Layout optimization: improving area, wire length, and delay through post-layout optimization and multi-objective design-space exploration.
Reproducible benchmarking: providing benchmark circuits, layout libraries, and tools for comparing design methods.

Software

Simon develops tools within the Munich Nanotech Toolkit (MNT) and contributes to the Munich Quantum Toolkit (MQT).

MQT Core: shared quantum computing infrastructure, including the MLIR/LLVM-based MQT Compiler Collection. His contributions cover compiler optimizations, gate synthesis, hardware-aware compilation, and integration with quantum programming frameworks.
MNT NanoPlaceR: reinforcement learning for placement and routing of field-coupled nanocomputing circuits.
MNT Designer: an interactive design environment for creating, optimizing, and verifying nanocomputing layouts.
MNT Bench: benchmarking software and layout libraries for evaluating field-coupled nanocomputing design algorithms, with a web interface.
fiction: an open-source framework for the design and simulation of field-coupled nanotechnologies, which he helps maintain.
MQT Bench: a quantum circuit benchmark suite, which he helps maintain, with a web interface.

Publications

Organized Tutorials, Workshops, Special Sessions, etc.

M. Walter, J. Drewniok, S. Hofmann, B. Hien, and R. Wille. The Munich Nanotech Toolkit (MNT). In IEEE International Conference on Nanotechnology (IEEE Nano). 2024. PDF.

Journals

S. S. H. Ng, M. Walter, S. Hofmann, J. Drewniok, R. Wille, and K. Walus. RTL-to-Atoms Synthesis of a Machine Learning Accelerator on Atomic-Scale Computers. IEEE Transactions on Nanotechnology (TNANO), 2026. DOI, PDF.

S. Hofmann, M. Walter, and R. Wille. Graph-Oriented Layout Design for Field-coupled Nanocomputing via Parallel Multi-Objective Search Space Exploration. IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), 2025. DOI, PDF.

S. Hofmann, M. Walter, and R. Wille. Efficient and Scalable Post-Layout Optimization for Field-coupled Nanotechnologies. IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems (TCAD), 2025. DOI, PDF.

Conferences, Workshops, etc. with Proceedings

P. Pham, A. Venkitaraman, S. Uhlich, C. Hsieh, A. Bonetti, M. Leibl, S. Hofmann, E. Ohbuchi, L. Servadei, U. Schlichtmann, and R. Wille. LASO-BOSS: LLM-driven Analog Sizing Optimization via Bayesian Optimization and Sizing Strategies. In International Symposium on Machine Learning for CAD (MLCAD). 2026. PDF.

S. Hofmann, L. Burgholzer, and R. Wille. Quantum Benchmark Generation for Software Evaluation, Hardware Design, and AI Training. In IEEE International Conference on Quantum Computing and Engineering (QCE). 2026. PDF.

S. S. H. Ng, M. Walter, S. Hofmann, J. Drewniok, R. Wille, and K. Walus. Design and Emulation Methodology for Atomic-Scale Systolic Arrays: An LLM Accelerator Case Study in Silicon DB Logic. In IEEE International Conference on Nanotechnology (IEEE Nano). 2026. DOI, PDF.

P. Pham, A. Venkitaraman, C. Hsieh, A. Bonetti, S. Uhlich, M. Leibl, S. Hofmann, E. Ohbuchi, L. Servadei, U. Schlichtmann, and R. Wille. GENIE-ASI: Generative Instruction and Executable Code for Analog Subcircuit Identification. In International Symposium on Machine Learning for CAD (MLCAD). 2025. PDF.

S. Hofmann, J. Drewniok, M. Walter, and R. Wille. MNT Designer: A Comprehensive Design Tool for Field-coupled Nanocomputing. In IEEE International Conference on Nanotechnology (IEEE Nano). 2025. DOI, PDF.

S. S. H. Ng, M. Walter, J. Drewniok, S. Hofmann, R. Wille, and K. Walus. Building a Machine Learning Accelerator with Silicon Dangling Bonds: From Verilog to Quantum Dot Layout. In IEEE International Conference on Nanotechnology (IEEE Nano). 2025. Received Best Student Paper Award. DOI, PDF.

B. Hien, M. Walter, S. Hofmann, and R. Wille. A Fully Planar Approach to Field-coupled Nanocomputing: Scalable Placement and Routing Without Wire Crossings. In IEEE International Conference on Nanotechnology (IEEE Nano). 2025. DOI, PDF.

S. Hofmann, M. Walter, and R. Wille. Late Breaking Results: Physical Co-Design for Field-coupled Nanocomputing. In Design, Automation and Test in Europe (DATE). 2025. DOI, PDF.

S. Hofmann, M. Walter, and R. Wille. Physical Design for Field-coupled Nanocomputing with Discretionary Cost Objectives. In IEEE Latin American Symposium on Circuits and Systems (LASCAS). 2025. Best Paper Award Candidate. DOI, PDF.

S. Hofmann, M. Walter, and R. Wille. A* is Born: Efficient and Scalable Physical Design for Field-coupled Nanocomputing. In IEEE International Conference on Nanotechnology (IEEE Nano). 2024. DOI, PDF.

M. Walter, J. Drewniok, S. Hofmann, B. Hien, and R. Wille. The Munich Nanotech Toolkit (MNT). In IEEE International Conference on Nanotechnology (IEEE Nano). 2024. DOI, PDF.

S. Hofmann, M. Walter, and R. Wille. Late Breaking Results: Wiring Reduction for Field-coupled Nanotechnologies. In Design Automation Conference (DAC). 2024. Acceptance rate: 21%. DOI, PDF.

S. Hofmann, M. Walter, L. Servadei, and R. Wille. Thinking Outside the Clock: Physical Design for Field-coupled Nanocomputing with Deep Reinforcement Learning. In International Symposium on Quality Electronic Design (ISQED). 2024. DOI, PDF.

S. Hofmann, M. Walter, and R. Wille. MNT Bench: Benchmarking Software and Layout Libraries for Field-coupled Nanocomputing. In Design, Automation and Test in Europe (DATE). 2024. DOI, PDF.

S. Hofmann, M. Walter, and R. Wille. Post-Layout Optimization for Field-coupled Nanotechnologies. In International Symposium on Nanoscale Architectures (NANOARCH). 2023. DOI, PDF.

S. Hofmann, M. Walter, L. Servadei, and R. Wille. Late Breaking Results From Hybrid Design Automation for Field-coupled Nanotechnologies. In Design Automation Conference (DAC). 2023. Acceptance rate: 23%. DOI, PDF.

S. Hofmann, M. Walter, and R. Wille. Scalable Physical Design for Silicon Dangling Bond Logic: How a 45° Turn Prevents the Reinvention of the Wheel. In IEEE International Conference on Nanotechnology (IEEE Nano). 2023. DOI, PDF.

Conferences, Workshops, etc. without Proceedings

S. Hofmann, M. Walter, L. Servadei, and R. Wille. Thinking Outside the Clock: Physical Design for Field-coupled Nanocomputing with Deep Reinforcement Learning. In International Workshop on Logic & Synthesis (IWLS). 2023.

Teaching

Courses

WT2024/25, ST2025, WT2025/26, ST2026
- Co-planning and course development, hands-on Python ML sessions, and occasional lectures for Machine Learning for Electronic Design Automation and Manufacturing.
WT2023/24, ST2024
- Hands-on Python ML exercises and occasional lectures for Machine Learning – Methods and Tools.
ST2025
- Logic Synthesis and Physical Design.
ST2024, ST2025
- Introduction to Emerging Technologies.

Awards

07/2025	Co-author of the Best Student Paper Award paper “Building a Machine Learning Accelerator with Silicon Dangling Bonds: From Verilog to Quantum Dot Layout”, published at the IEEE International Conference on Nanotechnology (IEEE NANO).
02/2025	Best Paper Award nomination for the paper “Physical Design for Field-coupled Nanocomputing with Discretionary Cost Objectives”, published at the IEEE Latin American Symposium on Circuits and Systems (LASCAS).
2018	Scholarship from the Lothar and Sigrid Rohde Foundation for exchange studies in Vietnam.

Curriculum Vitae

Personal Data

Name:	Simon Hofmann
Nationality:	German

Education

12/2022–01/2026	Doctorate (Dr.-Ing.), Computer Science, Technical University of Munich, Germany, summa cum laude
	Dissertation: Physical Design for Field-coupled Nanocomputing
10/2019–07/2022	Master's Degree (M.Sc.), Electrical and Computer Engineering, Technical University of Munich, Germany
	Master's Thesis: Reinforcement Learning and Evolutionary Algorithms for Jammer Mitigation in Cognitive Radio
10/2016–10/2019	Bachelor's Degree (B.Sc.), Electrical and Computer Engineering, Technical University of Munich, Germany
	Bachelor's Thesis: Hybrid Material Classification based on Deep Learning and Hand-Crafted Features
10/2018–01/2019	Exchange studies, RMIT University Vietnam, Ho Chi Minh City, Vietnam

Experience

since 02/2026	Postdoctoral Researcher, Chair for Design Automation, Technical University of Munich, Germany
since 01/2025	Senior Quantum Software Engineer, Munich Quantum Software Company (MQSC), Munich, Germany
12/2022–01/2026	Doctoral Researcher, Chair for Design Automation, Technical University of Munich, Germany
03/2022–05/2022	Research Assistant, Chair for Embedded Systems and Internet of Things, Technical University of Munich, Germany
	Developed rollback sequences for dynamic updates in industrial systems using Python.
01/2022–07/2022	Master's Thesis Researcher, Rohde & Schwarz, Munich, Germany
	Investigated reinforcement learning and evolutionary algorithms for jammer mitigation in cognitive radio.
10/2021–01/2022	Research Intern, Chair for Embedded Systems and Internet of Things, Technical University of Munich, Germany
	Optimized dynamic reconfiguration sequences in industrial systems using Python and SAT solving.
10/2019–03/2021	Working Student in Software Development, Rohde & Schwarz, Munich, Germany
	Developed Python software for automated testing of signal generators and analyzers.
03/2019–05/2019	Data Science Intern, Amanotes, Ho Chi Minh City, Vietnam
	Worked on data analysis, reporting, data pipelines, and cross-team data support for a music app startup.