Publications (42)
A performance characterization of quantum generative models
Carlos A. RiofrÃo, Oliver Mitevski, Caitlin Jones +6
Quantum generative modeling is a growing area of interest for industry-relevant applications. With the field still in its infancy, there are many competing techniques. This work is…
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…
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…
Exploring privacy-enhancing technologies in the automotive value chain
Gonzalo Munilla Garrido, Kaja Schmidt, Christopher Harth-Kitzerow +3
Privacy-enhancing technologies (PETs) are becoming increasingly crucial for addressing customer needs, security, privacy (e.g., enhancing anonymity and confidentiality), and regula…
Pilot-Data: An Abstraction for Distributed Data
Andre Luckow, Mark Santcroos, Ashley Zebrowski +1
Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, controlling co-placemen…
CRGC -- A Practical Framework for Constructing Reusable Garbled Circuits
Christopher Harth-Kitzerow, Georg Carle, Fan Fei +2
In this work, we introduce two schemes to construct reusable garbled circuits (RGCs) in the semi-honest setting. Our completely reusable garbled circuit (CRGC) scheme allows the ge…
Workflows Community Summit 2022: A Roadmap Revolution
Rafael Ferreira da Silva, Rosa M. Badia, Venkat Bala +102
Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex s…
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…
P*: A Model of Pilot-Abstractions
Andre Luckow, Mark Santcroos, Ole Weidner +3
Pilot-Jobs support effective distributed resource utilization, and are arguably one of the most widely-used distributed computing abstractions - as measured by the number and types…
Hybrid Quantum-HPC Middleware Systems for Adaptive Resource, Workload and Task Management
Pradeep Mantha, Florian J. Kiwit, Nishant Saurabh +2
Hybrid quantum-classical applications pose significant resource management challenges due to heterogeneity and dynamism in both infrastructure and workloads. Quantum-HPC environmen…
Automated structural testing of LLM-based agents: methods, framework, and case studies
Jens Kohl, Otto Kruse, Youssef Mostafa +9
LLM-based agents are rapidly being adopted across diverse domains. Since they interact with users without supervision, they must be tested extensively. Current testing approaches f…
Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
Florian J. Kiwit, Marwa Marso, Philipp Ross +3
Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training t…
Pilot-Quantum: A Quantum-HPC Middleware for Resource, Workload and Task Management
Pradeep Mantha, Florian J. Kiwit, Nishant Saurabh +2
As quantum hardware advances, integrating quantum processing units (QPUs) into HPC environments and managing diverse infrastructure and software stacks becomes increasingly essenti…
QISS: Quantum Industrial Shift Scheduling Algorithm
Anna M. Krol, Marvin Erdmann, Rajesh Mishra +5
In this paper, we show the design and implementation of a quantum algorithm for industrial shift scheduling (QISS), which uses Grover's adaptive search to tackle a common and impor…
Pilot-Streaming: A Stream Processing Framework for High-Performance Computing
Andre Luckow, George Chantzialexiou, Shantenu Jha
An increasing number of scientific applications rely on stream processing for generating timely insights from data feeds of scientific instruments, simulations, and Internet-of-Thi…
Task-parallel Analysis of Molecular Dynamics Trajectories
Ioannis Paraskevakos, Andre Luckow, Mahzad Khoshlessan +5
Different parallel frameworks for implementing data analysis applications have been proposed by the HPC and Big Data communities. In this paper, we investigate three task-parallel…
Deep Learning in the Automotive Industry: Applications and Tools
Andre Luckow, Matthew Cook, Nathan Ashcraft +3
Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, langu…
Introducing Distributed Dynamic Data-intensive (D3) Science: Understanding Applications and Infrastructure
Shantenu Jha, Daniel S. Katz, Andre Luckow +3
A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Data sets are growi…
Quantum Computing for Automotive Applications
Carlos A. RiofrÃo, 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…
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…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
QUARK: A Framework for Quantum Computing Application Benchmarking
Jernej Rudi Finžgar, Philipp Ross, Leonhard Hölscher +2
Quantum computing (QC) is anticipated to provide a speedup over classical HPC approaches for specific problems in optimization, simulation, and machine learning. With the advances…
Quantum Mini-Apps: A Framework for Developing and Benchmarking Quantum-HPC Applications
Nishant Saurabh, Pradeep Mantha, Florian J. Kiwit +2
With the increasing maturity and scale of quantum hardware and its integration into HPC systems, there is a need to develop robust techniques for developing, characterizing, and be…
Hadoop on HPC: Integrating Hadoop and Pilot-based Dynamic Resource Management
Andre Luckow, Ioannis Paraskevakos, George Chantzialexiou +1
High-performance computing platforms such as supercomputers have traditionally been designed to meet the compute demands of scientific applications. Consequently, they have been ar…
Pilot-Abstraction: A Valid Abstraction for Data-Intensive Applications on HPC, Hadoop and Cloud Infrastructures?
Andre Luckow, Pradeep Mantha, Shantenu Jha
HPC environments have traditionally been designed to meet the compute demand of scientific applications and data has only been a second order concern. With science moving toward da…
Performance Characterization and Modeling of Serverless and HPC Streaming Applications
Andre Luckow, Shantenu Jha
Experiment-in-the-Loop Computing (EILC) requires support for numerous types of processing and the management of heterogeneous infrastructure over a dynamic range of scales: from th…
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…
Quantum Computing: Towards Industry Reference Problems
Andre Luckow, Johannes Klepsch, Josef Pichlmeier
The complexity is increasing rapidly in many areas of the automotive industry. The design of an automobile involves many different engineering disciplines, e. g., mechanical, elect…
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…
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 Computing Techniques for Multi-Knapsack Problems
Abhishek Awasthi, Francesco Bär, Joseph Doetsch +16
Optimization problems are ubiquitous in various industrial settings, and multi-knapsack optimization is one recurrent task faced daily by several industries. The advent of quantum…
Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms
Martin J. A. Schuetz, J. Kyle Brubaker, Henry Montagu +6
We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble…
Methods and Experiences for Developing Abstractions for Data-intensive, Scientific Applications
Andre Luckow, Shantenu Jha
Developing software for scientific applications that require the integration of diverse types of computing, instruments, and data present challenges that are distinct from commerci…
A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures
Shantenu Jha, Judy Qiu, Andre Luckow +2
Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and schedu…
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…
Pilot-Edge: Distributed Resource Management Along the Edge-to-Cloud Continuum
Andre Luckow, Kartik Rattan, Shantenu Jha
Many science and industry IoT applications necessitate data processing across the edge-to-cloud continuum to meet performance, security, cost, and privacy requirements. However, di…
Algebraic multigrid support vector machines
Ehsan Sadrfaridpour, Sandeep Jeereddy, Ken Kennedy +3
The support vector machine is a flexible optimization-based technique widely used for classification problems. In practice, its training part becomes computationally expensive on l…
A Conceptual Architecture for a Quantum-HPC Middleware
Nishant Saurabh, Shantenu Jha, Andre Luckow
Quantum computing promises potential for science and industry by solving certain computationally complex problems faster than classical computers. Quantum computing systems evolved…
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…
Revealing the Landscape of Privacy-Enhancing Technologies in the Context of Data Markets for the IoT: A Systematic Literature Review
Gonzalo Munilla Garrido, Johannes Sedlmeir, Ãmer UludaÄ +3
IoT data markets in public and private institutions have become increasingly relevant in recent years because of their potential to improve data availability and unlock new busines…
Quantum State Preparation via Neural Network Encoding in Quantum Machine Learning
Kevin W. Aoun, Florian J. Kiwit, Carlos A. RiofrÃo +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…
Exploring Task Placement for Edge-to-Cloud Applications using Emulation
Andre Luckow, Kartik Rattan, Shantenu Jha
A vast and growing number of IoT applications connect physical devices, such as scientific instruments, technical equipment, machines, and cameras, across heterogenous infrastructu…