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Prof. Dr. Helena Liebelt

Faculty of Computer Science

Professors

Professor

Director Institute “Future Technologies” Founder and Director of Master HPC and Quantum Computing Founder and Director of Master Bachlor Data Center Management Member of the Examination Board Member of Faculty Council

Institut Future Technologies (Veilchengasse 2) - 3.002c

0991/3615-789

Sortierung:
Contribution
  • T. Fischer
  • A. Böttcher
  • A. Coday
  • Helena Liebelt

Defining and Measuring Performance Characteristics of Current Video Games.

In: Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance. (Lecture Notes in Computer Science) vol. 5987 pg. 120-135

  • Eds.:
  • D. Tygar
  • G. Weikum
  • F. Mattern
  • M. Vardi
  • T. Kanade
  • B. Steffen
  • D. Terzopoulos
  • J. Kleinberg
  • D. Hutchison
  • E. Rathgeb
  • J. Kittler
  • J. Mitchell
  • K. Echtle
  • O. Nierstrasz
  • M. Naor
  • M. Sudan
  • C. Pandu R.
  • B. Müller-Clostermann

Springer Verlag Berlin, Heidelberg

  • (2010)

DOI: 10.1007/978-3-642-12104-3_11

InternetDocument
  • Helena Liebelt

Help! What to do when the world is on fire. THD-Blog-Beitrag für die Kategorie „Digitale Wirtschaft und Gesellschaft“ (27.09.2021).

Technische Hochschule Deggendorf

  • (2021)

InternetDocument
  • Helena Liebelt

More than just a movie plot hole rescue. THD-Blog-Beitrag für die Kategorie „Digitale Wirtschaft und Gesellschaft“ (04.03.2021).

Technische Hochschule Deggendorf

  • (2021)

InternetDocument
  • Helena Liebelt

Connecting Quantum Computing & Women. THD-Blog-Beitrag für die Kategorie „Digitale Wirtschaft und Gesellschaft“ (08.03.2021).

Technische Hochschule Deggendorf

  • (2021)

InternetDocument
  • Helena Liebelt

In der digitalen Unterwelt. THD-Blog-Beitrag für die Kategorie „Digitale Wirtschaft und Gesellschaft“ (15.02.2021).

Technische Hochschule Deggendorf

  • (2021)

NewspaperArticle
  • Helena Liebelt

Evolution statt Revolution. Interview..

In: Managementkompass Quantencomputing (F.A.Z. Business Media GmbH - Ein Unternehmen der F.A.Z.-Gruppe, Frankfurt/Main) vol. 2021

  • 2021 (2021)
NewspaperArticle
  • Helena Liebelt

SheQuantum’s exclusive interview with Dr. Helena Liebelt, Professor of Computer Science @ Deggendorf Institute of Technology, Bavaria, Germany & Quantum Computing Lead @ Intel Labs Europe.

In: SheQuantum

  • 19.01.2021 (2021)

NewspaperArticle
  • Helena Liebelt

Viel mehr als Kopf und Zahl. Prof. Dr. Helena Liebelt von der THD erklärt Quantencomputer – Deggendorfer forschen daran mit. Interview..

In: Passauer Neue Presse

  • 01.02.2021 (2021)

NewspaperArticle
  • Helena Liebelt

Cyber-Sicherheitskonferenz: Die THD ist künftig dabei. Interview.

In: Passauer Neue Presse

  • 21.02.2021 (2021)

NewspaperArticle
  • Helena Liebelt

Ich habe ja nicht im Kaninchenstreicheln promoviert. Interview..

In: Passauer Neue Presse

  • 07.03.2021 (2021)

NewspaperArticle
  • Helena Liebelt

Als Frau in einer technischen Branche: Prof. Liebelt im Interview.

In: Passauer Neue Presse

  • 08.03.2021 (2021)

Contribution
  • Peter Faber
  • Helena Liebelt

High-Performance and Quantum Computing for Students.

In: Informatik 2022 - Informatik in den Naturwissenschaften. vol. P-326 pg. 1085-1091

  • Eds.:
  • D. Krupka
  • D. Demmler
  • H. Federrath

Gesellschaft für Informatik Bonn

  • (2022)
Lecture
  • Helena Liebelt

Quantencomputer - die Revolution in der IT.

In: Lion's Club Deggendorf

Online

  • 25.01.2022 (2022)
Lecture
  • Helena Liebelt

Digitalisierung - Chancen & Risiken.

In: Besuch der Staatsministerin für Digitales Judith Gerlach

Deggendorf

  • 31.03.2022 (2022)
Lecture
  • Helena Liebelt

Girl's Day an der THD - MINT.

In: Girl's Day 2022

Technische Hochschule Deggendorf Deggendorf

  • 28.04.2022 (2022)
Lecture
  • Helena Liebelt

Präsentation des neuen Studiengangs "Data Center Management".

In: Data Centre World Frankfurt 2022

Frankfurt am Main

  • 11.05.2022 (2022)
Lecture
  • Helena Liebelt

Promoting the visibility of women in the HPC community.

In: ISC High Performance 2022

Hamburg

  • 02.06.2022 (2022)
Lecture
  • Helena Liebelt

HPC: Schrödingers Katze - Tot oder Leben.

In: Science Bench im Rahmen des Projekts TRIO (Transfer und Innovation Ostbayern)

Deggendorf

  • 04.07.2022 (2022)
Lecture
  • Helena Liebelt

Smart City aus der Perspektive der Wissenschaft.

In: Stadt Regensburg, Amt für Wirtschaft und Wissenschaft

Regensburg

  • 05.07.2022 (2022)
Lecture
  • Helena Liebelt

Digitalisierung - Chancen & Risiken.

In: Mitgliederversammlung des THD-Fördervereins

Deggendorf

  • 07.07.2022 (2022)
Lecture
  • Helena Liebelt

Zentrum für Digitalisierung.

In: Forschungsklausur der THD

Mariakirchen

  • 18.07.2022 (2022)
Lecture
  • Helena Liebelt

Jubiläumsfeier 10 Jahre VIRZ.

In: 10-jähriges Jubliläum des Verbands Innovatives Rechenzentrum e.V. (VIRZ)

Online

  • 21.10.2022 (2022)
Lecture
  • Helena Liebelt

Keynote Speech - Quantum Technologies: Areas of improvement or how not to slide into quantum winter.

In: 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022)

CERN - European Organization for Nuclear Research Bari, Italy

  • 23.-28.10.2022 (2022)

Lecture
  • Helena Liebelt

Bavarian Quantum Computing Exchange - Intel Quantum SDK.

In: 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022)

Bari, Italy

  • 27.10.2022 (2022)
Lecture
  • T. Shinde
  • Helena Liebelt
  • Rui Li

Preliminary Lattice Boltzmann Method Simulation using Intel® Quantum SDK.

In: 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022)

CERN - European Organization for Nuclear Research Bari, Italy

  • 27.10.2022 (2022)

show abstract

The present work is based on the research within the framework of cooperation between Intel Labs and Deggendorf Institute of Technology, since the Intel® Quantum SDK (Software Development Kit) has recently released. Transport phenomena e.g. heat transfer and mass transfer are nowadays the most challenging unsolved problems in computational physics due to the inherent nature of fluid complexity. As the revolutionary technology, quantum computing opens a grand new perspective for numerical simulation including the computational fluid dynamics (CFD). It is true that the current CFD algorithms based on the different scales (e.g. macroscopic or microscopic) need to be translated into quantum system. In the current work the quantum algorithms have been preliminarily implemented for fluid dynamics using the Intel Quantum SDK, one mesoscopic approach has been applied i.e. to solve the lattice Boltzmann equation. Taking the simplest transport phenomena as a starting point, the preliminary quantum simulation results have been validated with the analytical solution and the classical numerical simulation. The potential of quantum in simulating fluid will be discussed.
Lecture
  • Helena Liebelt

Präsentationen der einzelnen Vortragenden mit Hauptmoderation der THD Professoren.

In: BQCX-Veranstaltung (Bavarian Quantum Computing eXchange)

Leibniz Rechenzentrum München

  • 09.11.2022 (2022)
Lecture
  • Helena Liebelt

Workshop (13.11.2022) und Invited Talk ("Tools to Level Up Your Career and Personal Well-Being").

In: Supercomputing (SC) 22 - The International Conference for High Performance Computing, Networking, Storage, and Analysis

Dallas, TX, USA

  • 13.-18.11.2022 (2022)
Contribution
  • M. Abuzayed
  • Helena Liebelt
  • Rui Li

Novel Comparison of Noise Models Simulation with Runs on Quantum Computer. Abstract ID: P-O-07.

In: Book of Abstracts. pg. 85

  • Eds.:
  • P. Imhof
  • T. Kühne
  • F. Wolf
  • S. Wolf
  • M. Schroeder

  • (2023)

show abstract

Noise significantly impacts the performance of quantum computers, making it imperative to consider its effects in discussions about practical quantum computing. Benchmarking approaches cannot succeed without addressing noise and its impact on measurements. Comparing noise models with real quantum computer runs helps validate simulations and understand hardware limitations. Accurately simulating a quantum processor requires realistic noise models, but identifying the appropriate model and performing the simulation computationally is challenging. This proposed work is part of a collaborative research effort involving the Deggendorf Institute of Technology, Leibniz Supercomputer Center (LRZ), Atos, and IQM. Firstly, we will deploy a quantum circuit with the noise model of the IQM superconducting 5-qubit system on the Quantum Learning Machine (QLM) emulator, then on the real quantum hardware. The unique advantage of both the QLM and the IQM 5-qubit system being located at LRZ premises is the opportunity to compare algorithm simulations on the QLM with the newly developed noise model to runs on real hardware using the same algorithms. The primary aim of this research is to utilize the quantum emulator (QLM) for benchmarking real hardware at LRZ. The comparison will involve characterizing and modeling the noise processes in the hardware to improve the models for realistic simulation of digital quantum circuits. This study focuses on accurately modeling quantum noise and assessing the robustness of quantum operations. By doing so, the research aims to understand the impact of the noise model on the solution, identify differences from the ideal solution, and analyze the introduced errors.
Contribution
  • Tejas Shinde
  • Yaknan Gambo
  • K. Rasch
  • Helena Liebelt
  • Rui Li

Solving 2 by 2 Grid Sudoku Problem using Grover’s Algorithm with Intel Quantum SDK.

In: 2023 13th International Conference on Advanced Computer Information Technologies (ACIT). pg. 432-435

IEEE

  • (2023)

DOI: 10.1109/ACIT58437.2023.10275442

show abstract

Quantum technologies are moving towards enabling real world uses and as these technologies develop. The most important thing that differs Quantum from classical computers is that Quantum Computers can do “multiple” tasks at the same time as opposed to the classical computers doing a single task at a time. This phenomenon has led to the discovery of a few algorithms that are faster than regular computers. One such algorithm is the Grover’s Algorithm[l]. This algorithm has shown a few real-world applications such as Quantum Search Algorithm, Travelling salesman, etc[2]. In this paper, Grover’s algorithm is introduced, and a 2 x 2 Sudoku example is solved using Intel Quantum Software Development Kit[3]. The Intel Quantum SDK is quite new and in this paper we show how a simple Grover’s Problem can be implemented in the SDK. Sudoku is a popular number-based puzzle game played on a grid consisting of nine squares subdivided into smaller 3x3 grids. The objective is to fill the grid with numbers from 1 to 9, ensuring that each row, column, and 3x3 grid contains all numbers exactly once.
Contribution
  • Yaknan Gambo
  • Tejas Shinde
  • K. Rasch
  • Helena Liebelt
  • Rui Li

Simulation of the Quantum Key Distribution Algorithm Using the Intel Quantum SDK.

In: 2023 13th International Conference on Advanced Computer Information Technologies (ACIT). pg. 492-495

IEEE

  • (2023)

DOI: 10.1109/ACIT58437.2023.10275447

show abstract

Information security is an important fabric of our world today. Technologies used in the development of information security have constantly been evolving in response to new threats. Cryptographic algorithms like the RSA, DSA, AES etc. are currently being used in securing information even over insecure channels. The coming of quantum computers has now posed a great threat to classical cryptosystems which can be broken in polynomial time thanks to Shor’s algorithm. A good hacker with good quantum computing power can break into encrypted messages easily. The Quantum Key Distribution (QKD) algorithm based on the BB84 protocol provides a solution to this problem. It makes it possible for communicating parties to exchange secret keys securely before a conversation can ensue. Verifying if these keys have been tampered with is a good first step to start before any other. QKD technology has made it possible to exchange these keys via an insecure channel and lets communicating parties know if an eavesdropper has tempered with the message. In this work, we discuss the QKD algorithm and simulated it on the newly released Intel quantum SDK.
NewspaperArticle
  • J. Schröper
  • Helena Liebelt

Tech Forum der LANline in München - Erfolgreiches Branchentreffen. (Interview zwischen WEKO Fachmedien und Prof. Dr. Liebelt).

In: LANline (IT, Network, Datacenter) vol. 2023 pg. 6-7

  • 02.03.2023 (2023)

Lecture
  • Helena Liebelt

Quantum Technologies: Areas of Improvement or How not to Slide into Quantum Winter.

In: International Conference on Quantum Physics and Nuclear Technology

Paris, France; Online

  • 27.03.2023 (2023)
Lecture
  • Helena Liebelt

Quantum Computing - from hype to reality.

Albert-Ludwigs-Universität Freiburg im Breisgau

  • 04.04.2023 (2023)
Lecture
  • Rui Li
  • Helena Liebelt

Quantum Computing Education.

In: World of QUANTUM 2023/ Parallel zur LASER World of PHOTONICS 2023

München

  • 24.-27.06.2023 (2023)
Lecture
  • Helena Liebelt

Quantum Technologies: Areas of improvement or how not to slide into quantum winter.

In: Data Science Week 2023 - World Conference on Data Science and Statistics: "Understanding the Data Science: Can help now and in the Future”

Frankfurt am Main

  • 27.06.2023 (2023)
Lecture
  • Helena Liebelt
  • B. Pötter
  • B. Wellmann
  • P. Wolff

The Quantum Classroom: Challenges and Opportunities in Quantum Computing Education. Panel Discussion.

In: Quantum Summit 2023: Conference on the potential of quantum computing technology

Berlin

  • 20.09.2023 (2023)
Lecture
  • Helena Liebelt

Bewältigung des Fachkräftemangels (Podiumsdiskussion).

In: Bitkom Quantum Summit 2023

Berlin

  • 21.09.2023 (2023)

show abstract

In der Hauptstadt war die Deggendorferin Liebelt am 21. September als Referentin wie auch als Expertin während einer Podiumsdiskussion zum Thema „Bewältigung des Fachkräftemangels“ gefragt. In ihrem Vortrag stellte Professorin Liebelt den Masterstudiengang für Quantum Computing der Hochschule in Deggendorf vor. Diesen hatte sie auf Basis ihrer umfangreichen Industrieerfahrung selbst konzipiert, „von Partnern und Arbeitgebern wurde er erfreulicherweise als der Goldstandard in der Ausbildung des Quantum Computings gefeiert“, wie Liebelt aus Berlin berichtet. Die Erfolge könnten sich denn auch sehen lassen. Die THD-Professorin präsentierte beim Bitkom Summit neueste Entwicklungen und betonte „das immense Potenzial dieses Studiengangs für den Quantum Arbeitsmarkt“. Ihr Vortrag sei von den Teilnehmern als inspirierend und informativ wahrgenommen und habe zur Diskussion über Lösungsansätze für den Fachkräftemangel beigetragen.
Lecture
  • Helena Liebelt

Bereit für die Zukunft. Keynote.

In: IBM Quantum Industry Exchange

Ehningen

  • 26.10.2023

show abstract

In unserem Vortrag untersuchen wir die Anforderungen, die angehende Quantum Computing-Experten erfüllen müssen, und heben den entscheidenden Unterschied zwischen herkömmlicher Computertechnologie und Quantum Computing hervor. Dabei zeigen wir auf, wie diese Anforderungen im Rahmen des Master-Programms an der Technischen Hochschule Deggendorf (THD) umgesetzt werden. Darüber hinaus präsentieren wir das junge 'Institut für Zukunftstechnologien' an der THD, das sich auf die Anpassung und Optimierung neuer Technologien spezialisiert hat. Als Beispiel aktueller Forschung stellen wir aktuelle Fortschritte in 'Fluid Dynamics in Quantum Computing'. Lassen Sie uns gemeinsam in die Zukunft der Technologie eintauchen und die Möglichkeiten erkunden, die sich vor uns auftun. Wir freuen uns auf inspirierende Diskussionen und den Austausch von Ideen!
Contribution
  • Suhaib Al-Rousan
  • Rui Li
  • Helena Liebelt

An Extensive Review of Quantum Circuit Cutting.

In: 2nd NHR Conference (Nationales Hochleistungsrechnen) - Computational Engineering, Materials Science, Simulation & AI - Book of Abstracts. pg. 82

  • (2024)
Contribution
  • Shradda Thanki
  • Helena Liebelt
  • Rui Li

Quantum Lattice Boltzmann Method in Intel Quantum SDK.

In: Proceedings of the International Supercomputing Conference (ISC).

  • (2024)

show abstract

Generally, Partial Differential Equations (PDEs) are used in describing physical processes e.g. Fluid Flow. PDEs can be transformed into algebraic equations and then using computers to solve them. The algebraic equations transformed into their discrete counterparts are calculated at each computational point. The more complex the physical system, the larger the dimensionality of the algebraic equations, the more computational power required. It seems that Quantum Computers could help us solve this problem. We have tried and implemented a simple 1-dimensional, 2-Dimensional, and 3-Dimensional advection-diffusion equation using the Quantum Lattice Boltzmann Method (LBM) algorithm in the Intel Quantum Software Development Kit (SDK). The highlight of this work is to involve quantum Walk.
InternetDocument
  • Helena Liebelt
  • Jörg Kunz

DIT-professor Helena Liebelt winning at the Women in Tech Global Awards 2024.

idw - Informationsdienst Wissenschaft

  • (2024)

Contribution
  • Suhaib Al-Rousan
  • Rui Li
  • Helena Liebelt

Quantum Circuit Decomposition for Quantum Image Representation: Reducing Circuit Depth with One Example.

In: 2nd NHR Conference (Nationales Hochleistungsrechnen) - Computational Engineering, Materials Science, Simulation & AI - Book of Abstracts. pg. 81

  • (2024)

show abstract

Classical computers for certain types of problems, with the potential for polynomial or even exponential improvements. Nevertheless, the existing quantum computing hardware, commonly known as Noisy Intermediate-Scale Quantum (NISQ) computers, encounters substantial obstacles concerning scalability and reliability. In order to address these problems, the quantum algorithm community has been increasingly relying on quantum-classical hybrid algorithms, which utilize classical resources to assist in quantum computations. An important advancement in this field is quantum circuit cutting, which entails dividing quantum circuits into smaller, autonomous parts. This approach not only enables the ability to scale quantum experiments, but also improves the accuracy and resilience to noise of large circuits by analyzing smaller subcircuits. Given the importance of this technique, our review paper summarizes the recent advancements in quantum circuit cutting methods. We define various cutting methods, analyze their computational complexities, and provide an extensive review of their effectiveness. By investigating the similarities and differences between these methods, we offer a comprehensive understanding of the current state and future directions of quantum circuit cutting.
Contribution
  • Digvijaysinh Ajarekar
  • Sabiya Shaikh
  • Neha Khachibhoya
  • Helena Liebelt
  • Rui Li

Efficient Encoding and Retrieval of Audio Signals in Quantum Circuits using NAQSS.

In: 2nd NHR Conference (Nationales Hochleistungsrechnen) - Computational Engineering, Materials Science, Simulation & AI - Book of Abstracts. pg. 83

  • (2024)
Contribution
  • Digvijaysinh Ajarekar
  • Suhaib Al-Rousan
  • Shradda Thanki
  • Helena Liebelt
  • Rui Li

From Classical via Hybrid to Quantum model: Quantum Machine Learning Applications for Fake Art Identification.

In: Quantum Matter International Conference (QUANTUMatter 2024) Book of Abstracts.

  • (2024)

show abstract

Artificial Intelligence has been used for the real and fake art identification and different machine learning (ML) models are being trained then employed with acceptable accuracy in classifying artworks. Fake art can distort the understanding and appreciation of an artist's true work and style. Accurate identification of genuine pieces ensures the preservation of artistic heritage and prevents the spread of misinformation. As the future revolutionary technology, quantum computing opens a grand new perspective in the art area. Using Quantum Machine Learning(QML), the current work explores the utilization of fully quantum models, hybrid models along with classical model implementation. The study utilizes Normal Arbitrary Superposition state (NAQSS) for encoding image into quantum circuit. The learning of trainable parameters for image classification Quantum Neural Networks (QNN) for a fully quantum models. With a Hybrid approach, Hybrid Quantum Neural Network with parallel quantum dense layers (HQNN-Parallel). ResNet model is being used for classical model. The study addresses the quantum speed up in training time of models, accuracy and computational complexity of the models. Starting with a simplest example of 4 * 4 images up to 32 * 32, the accuracy has been improved for full quantum model with the increasing size of the images as the circuit depth increases linearly with the image size namely (2 n+1− 1). The three models are discussed and the potential of QML and parameters influencing accuracy are extensively investigated. The implementations have been carried out using Qiskit and Torch for training the models.
Lecture
  • Helena Liebelt

Quanten-Computing und die Auswirkungen auf die RZ-Technik. Keynote.

In: Tech Forum München

München

  • 06.02.2024
Lecture
  • Helena Liebelt
  • Rui Li
  • Digvijaysinh Ajarekar
  • Suhaib Al-Rousan

Advancing Image Classification using Intel SDK: Integrating NAQSS Encoding with Hybrid Quantum-Classical PQC Models.

In: 22nd International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2024)

Charles B. Wang Center, Stony Brook University Stony Brook, NY, NY

  • 11.03.2024 (2024)

show abstract

Artificial intelligence has been used for the real and fake art identification and different machine learning models are being trained then employed with acceptable accuracy in classifying artworks. As the future revolutionary technology, quantum computing opens a grand new perspective in the art area. Using Quantum Machine Learning (QML), the current work explores the utilization of Normal Arbitrary Quantum Superposition State (NAQSS) for encoding images into a quantum circuit. The learning of trainable parameters for image classification is achieved through the use of layers of Parameterized Quantum Circuit (PQC) with a hybrid optimizer. Starting with the simplest example i.e. 2x2-colored images, the accuracy has been improved with the increasing size of the images, as the circuit depth increases linearly with the image size namely quantum gates. The potential of QML and parameters influencing accuracy are extensively investigated. The implementations have been carried out using the Intel Quantum SDK (Software Development Kit), based on the research within the framework of cooperation between Intel Labs and Deggendorf Institute of Technology.
Lecture
  • Helena Liebelt

Quantentechnologie in Deutschland.

In: Advanced Computing for AI: Potenziale von HPC und neuen Rechentechnologien

Bitkom München

  • 18.04.2024 (2024)
Lecture
  • Helena Liebelt

Supercomputing 2024. Keynote.

In: SC24 - The International Conference for High Performance Computing, Networking, Storage, and Analysis

Atlanta, GA, USA

  • 18.11.2024 (2024)
Journal article
  • T. Shinde
  • L. Budinski
  • O. Niemimäki
  • V. Lahtinen
  • Helena Liebelt
  • Rui Li

Utilizing classical programming principles in the Intel Quantum SDK: implementation of quantum lattice Boltzmann method.

In: ACM Transactions on Quantum Computing vol. 6 pg. 1-18

  • (2025)

DOI: 10.1145/3678185

show abstract

We explore the use of classical programming techniques in implementing the quantum lattice Boltzmann method in the Intel Quantum SDK – a software tool for quantum circuit creation and execution on Intel quantum hardware. As hardware access is limited, we use the state vector simulator provided by the SDK. The novelty of this work lies in leveraging classical techniques for the implementation of quantum algorithms. We emphasize the refinement of algorithm implementation and devise strategies to enhance quantum circuits for better control over problem variables. To this end, we adopt classical principles such as modularization, which allows for systematic and controlled execution of complex algorithms. Furthermore, we discuss how the same implementation could be expanded from state vector simulations to execution on quantum hardware with minor adjustments in these configurations.

core competencies

High Performance Computing Quantum Computing Data Center Management Cyber Security