15 citations · 32 across the 11 of their papers we have counts for
11 papers
Quantum State Preparation via Neural Network Encoding in Quantum Machine Learning
Kevin W. Aoun, Florian J. Kiwit, Carlos A. Riofrío +4
A central challenge in quantum machine learning is the state preparation bottleneck that describes the prohibitive computational cost of loading high-dimensional classical data int…
Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications
Jakob Sturm, Josef Pichlmeier, Christian Bernhard +4
Large Language Models (LLMs) are increasingly employed in enterprise question-answering (QA) systems, requiring adaptation to domain-specific knowledge. Among the most prevalent me…
Enhancing Generative AI Image Refinement with Scribbles and Annotations: A Comparative Study of Multimodal Prompts
Hyerim Park, Phuong Thao Tran, Andre Luckow +3
Generative AI (GenAI) image tools are increasingly used in design practice, enabling rapid ideation but offering limited support for refinement tasks such as adjusting layout, scal…
Typical Machine Learning Datasets as Low-Depth Quantum Circuits
Florian J. Kiwit, Bernhard Jobst, Andre Luckow +2
Quantum machine learning (QML) is an emerging field that investigates the capabilities of quantum computers for learning tasks. While QML models can theoretically offer advantages…
Exploring Visual Prompts: Refining Images with Scribbles and Annotations in Generative AI Image Tools
Hyerim Park, Malin Eiband, Andre Luckow +1
Generative AI (GenAI) tools are increasingly integrated into design workflows. While text prompts remain the primary input method for GenAI image tools, designers often struggle to…
Quantum Computing for Automotive Applications
Carlos A. Riofrío, Johannes Klepsch, Jernej Rudi Finžgar +7
Quantum computing could impact various industries, with the automotive industry with many computational challenges, from optimizing supply chains and manufacturing to vehicle engin…
Assessing the Requirements for Industry Relevant Quantum Computation
Anna M. Krol, Marvin Erdmann, Ewan Munro +2
In this paper, we use open-source tools to perform quantum resource estimation to assess the requirements for industry-relevant quantum computation. Our analysis uses the problem o…
Quantum-Inspired Fluid Simulation of 2D Turbulence with GPU Acceleration
Leonhard Hölscher, Pooja Rao, Lukas Müller +4
Tensor network algorithms can efficiently simulate complex quantum many-body systems by utilizing knowledge of their structure and entanglement. These methodologies have been adapt…
Performance Characterization of Expert Router for Scalable LLM Inference
Josef Pichlmeier, Philipp Ross, Andre Luckow
Large Language Models (LLMs) have experienced widespread adoption across scientific and industrial domains due to their versatility and utility for diverse tasks. Nevertheless, dep…
Towards Application-Aware Quantum Circuit Compilation
Nils Quetschlich, Florian J. Kiwit, Maximilian A. Wolf +4
Quantum computing has made tremendous improvements in both software and hardware that have sparked interest in academia and industry to realize quantum computing applications. To t…
Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK
Florian J. Kiwit, Maximilian A. Wolf, Marwa Marso +4
Quantum computing promises a disruptive impact on machine learning algorithms, taking advantage of the exponentially large Hilbert space available. However, it is not clear how to…