Robert Wille

Full and Distinguished Professor

Technical University of Munich

Germany

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Robert Wille is a Full and Distinguished Professor at the Technical University of Munich, Germany. His research areas encompass field-coupled nanotechnologies, deep learning applications in radar sensing, and quantum circuit design. Recent articles authored by Professor Wille include topics on post-layout optimization, artifact rejection in radar systems, and automated design for complex systems like multiorgan-on-chip geometries.

Robert Wille

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Articles (20)

Automated Synthesis of Fault-Tolerant State Preparation Circuits for Quantum Error-Correction Codes

Efficient and Scalable Post-Layout Optimization for Field-Coupled Nanotechnologies

How to Talk to Your Classifier: Conditional Text Generation with Radar–Visual Latent Space

Deep Learning Classifier for Robust Artifact Rejection in FMCW Radar Vital Sensing

Forward and Backward Constrained Bisimulations for Quantum Circuits Using Decision Diagrams

Equivalence checking of quantum circuits via intermediary matrix product operator

Multirange Breathing Rate Estimation With Deep Learning Using FMCW Radar

Automated Design for Multiorgan-on-Chip Geometries

Shuttling for Scalable Trapped-Ion Quantum Computers

MQT Core: The Backbone of the Munich Quantum Toolkit (MQT)

Model‐driven engineering of safety and security software systems: A systematic mapping study and future research directions

Postpandemic Conferences: The DATE 2023 Experience

Design Automation for Continuous-Flow Lab-on-a-Chip Systems: A One-Pass Paradigm

On Optimal Subarchitectures for Quantum Circuit Mapping

Robust Radar-Based Vital Sensing With Adaptive Sinc Filtering and Random Body Motion Rejections

Simulation Paths for Quantum Circuit Simulation With Decision Diagrams What to Learn From Tensor Networks, and What Not

A Comprehensive Survey on Electronic Design Automation and Graph Neural Networks: Theory and Applications

Noise-Aware Quantum Circuit Simulation With Decision Diagrams

Channel Routing for Microfluidic Devices: A Comprehensive and Accessible Design Tool

Deep Reinforcement Learning for Optimization at Early Design Stages

Collaborators (12)

Avik Santra

Andy Pimentel

Atif Mashkoor

Ulf Schlichtmann

Maria Emmerich

Wolfgang Ecker

Xing Huang

Lucas Berent

Yannick Stade

Lukas Burgholzer

Sebastian Stock

Sepp Hochreiter

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