Laboratory for Digitalisation — Prof. Dr. Wolfgang Mauerer

Unter Beteiligung des bayerischen Wirtschaftsministers Hubert Aiwanger wurde der Öffentlichkeit an der TechBase in Regensburg ein neues Kompetenzzentrum zum Thema Digitalisierung präsentiert. Bei dem Projekt Digital Innovation Ostbayern (DInO) handelt es sich dabei um eines von drei European Digital Innovation Hubs (EDIH) in Bayern. Die Projektpartner der TH Deggendorf, OTH Regensburg, R-Tech GmbH und die Bayerische KI-Agentur „baiosphere“ unterstützen und beraten im Rahmen dieses Projekts kleinere und mittlere Unternehmen sowie öffentliche Einrichtungen bei ihren digitalen Herausforderungen.
Das Labor für Digitales (LfD) ist dabei aufgrund seiner langjährigen und domänenübergreifenden Expertise verantwortlich für die Schwerpunktthemen Künstliche Intelligenz, maschinelles Lernen und Datenanalyse innerhalb des Projekts. Benno Bielmeier und Wolfgang Mauerer unterstützen anwendungsorientiert bei der praktischen Erprobung von Risiken, der Sicherheit und der Qualitätssicherung von nachhaltigen KI-Konzepten und -Lösungen. Dabei werden Aspekte der Auswirkung auf Wirtschaft, Politik und Gesellschaft holistisch beachtet und eine Brücke zwischen aktuellem Forschungsstand und praktischen Anforderungen geschlagen, um transformative Innovation und Digitalisierung voranzutreiben.

New contribution to the CORE A* VLDB conference

Our leading research on quantum data management, driven by Manuel Schönberger, Immanuel Trummer, head of the Cornell Database Group, and Wolfgang Mauerer, uncovers the potential of quantum computing for databases. In our latest paper, accepted for publication in PVLDB and to be presented at VLDB'24, we derive a novel, tailored encoding method to enable the use of highly optimised, quantum-inspired Fujitsu Digital Annealer hardware to solve large instances of the long-standing join ordering problem.

Abstract
Finding the optimal join order (JO) is one of the most important problems in query optimisation, and has been extensively considered in research and practise. As it involves huge search spaces, approximation approaches and heuristics are commonly used, which explore a reduced solution space at the cost of solution quality. To explore even large JO search spaces, we may consider special-purpose software, such as mixed-integer linear programming (MILP) solvers, which have successfully solved JO problems. However, even mature solvers cannot overcome the limitations of conventional hardware prompted by the end of Moore’s law.
We consider quantum-inspired digital annealing hardware, which takes inspiration from quantum processing units (QPUs). Unlike QPUs, which likely remain limited in size and reliability in the near and mid-term future, the digital annealer (DA) can solve large instances of mathematically encoded optimisation problems today. We derive a novel, native encoding for the JO problem tailored to this class of machines that substantially improves over known MILP and quantum-based encodings, and reduces encoding size over the state-of-the-art. By augmenting the computation with a novel readout method, we derive valid join orders for each solution obtained by the (probabilistically operating) DA. Most importantly and despite an extremely large solution space, our approach scales to practically relevant dimensions of around 50 relations and improves result quality over conventionally employed approaches, adding a novel alternative to solving the long-standing JO problem.

The new supplementary study programme "Quantum Technologies" will start for the first time in winter semester 2023/24!
Quantum technologies hold immense potential for solving the most complex problems that have so far pushed classical computers to their limits. The supplementary course offers students a gentle introduction to this complex topic -- from the basics of fascinating phenomena of quantum mechanics, their exploitation in cryptography to the simulation of quantum circuits and problem formulations for quantum computers. The study programme prepares students in the best possible way for the technology of the future! Registration for the first round is now possible via the website of the Regensburg School for Digital Sciences (RSDS).
The additional study programme is an interdisciplinary initiative of Prof. Dr. Ioana Serban (Faculty of Applied Natural and Cultural Sciences), Prof. Dr. Jürgen Mottok (LaS³, Faculty of Electrical and Information Technologies) and Prof. Dr. Wolfgang Mauerer (LfD, Faculty of Computer Science and Mathematics).

Das neue Zusatzstudium "Quantentechnologien" startet zum ersten Mal im Wintersemester 2023/24!
Quantentechnologien bergen ein imenses Potenzial für die Lösung komplexester Probleme, die klassische Computer bislang an ihre Grenzen bringen. Das Zusatzstudium bietet Studierenden eine sanfte Hinführung zu der komplex erscheinenden Thematik -- angefangen von den Grundlagen faszinierender Phänomene der Quantenmechanik über deren Ausnutzung in der Kryptographie bis hin zur Simulation von Quantenschaltkreisen und Problemformulierungen für Quantencomputer. Damit bereitet das Zusatzstudium Studierende bestmöglich auf die Zukunftstechnologie vor! Die Anmeldung für den ersten Durchlauf ist ab sofort über die Website der Regensburg School for Digital Sciences (RSDS) möglich!
Das Zusatzstudium ist eine interdisziplinäre Initiative von Prof. Dr. Ioana Serban (Fakultät Angewandte Natur- und Kulturwissenschaften), Prof. Dr. Jürgen Mottok (LaS³, Fakultät Elektro- und Informationstechnologien) und Prof. Dr. Wolfgang Mauerer (LfD, Fakultät Informatik und Mathematik).

Wolfgang Mauerer and colleagues from the QLindA consortium hosted a workshop on Quantum Machine Learning (QML) as part of the IEEE Conference on Quantum Computing and Engineering to to discuss challenges and applications of QML in Bellevue, Washington, USA.

International researchers from all over the world participated enthusiastically in the workshop and exchanged ideas on the latest advances in Quantum Machine Learning.

We are thrilled to actively contribute to these pioneering efforts!

The LfD Quantum booth at the World of Quantum Fair from 27th to 30th of June in Munich attracted many curious visitors and ensured a wide range of conversations on the topic.

The corresponding After-Show-Party was well received, sparked engaging discussions about the future of quantum computing and helped initiating new interdisciplinary collaborations.

Advancing Quantum Software Engineering at IEEE QSW'23

Two papers by Felix Greiwe, Tom Krüger and Hila Safi, the latter in joint work with Karen Wintersperger, have been accepted at the IEEE Quantum Software Week. They deal with the role of noise and imperfections in quantum software engineering, and uncover generic patterns in the performance of systems optimised by HW/SW co-design approaches. Congrats, Felix, Tom and Hila!
The papers arose of the BMBF sponsored project TAQO-PAM. Of course, both are accompanied by extensive reproduction packages that allow independent researchers to confirm our results.

Our work in developing quantum applications for industry is presented by the Bavarian Ministry of Science as research highlight in Bavaria.

Verband der Elektro- und Digitalindustrie, Arbeitskreis Funktionale Sicherheit ISO 26262 und Untergruppe Software

Abstract
Consolidation of multiple systems of different criticality to one platform of mixed-criticality is an ongoing trend in various embedded industries due to the availability of powerful multicore processors. The isolation of different computing domains is the most crucial factor to guarantee freedom from interference. In this talk, Ralf Ramsauer presents the current state of Static Hardware Partitioning, a technique that leverages virtualisation extensions of modern CPUs to strongly isolate different computing domains on SMP platforms. He shows that it is possible to virtualise embedded real-time systems with (almost) zero runtime overhead and software interaction.

International Workshop on Quantum Data Science and Management organised by Wolfgang Mauerer jointly with Sven Groppe (University of Lübeck), Jiaheng Lu (University of Helsinki), and Le Gruenwald (University of Oklahoma) at the 49th International Conference on Very Large Data Bases.

Goals of the Workshop
For most database researchers, quantum computing and quantum machine lerning are still new research fields. The goal of this workshop is to bring together academic researchers and industry practitioners from multiple disciplines (e.g., database, AI, software, physics, etc.) to discuss the challenges, solutions, and applications of quantum computing and quantum machine learning that have the potential to advance the state of the art of data science and data management technologies. Our purpose is to foster the interaction between database researchers and more traditional quantum disciplines, as well as industrial users. The workshop serves as a forum for the growing quantum computing community to connect with database researchers to discuss the wider questions and applications of how quantum resources can benefit data science and data management tasks, and how quantum software can support this endeavor.

Contribution at the ACM SIGMOD/PODS International Conference on Management of Data by Tobias Winker, Sven Groppe (University of Lübeck), Valter Uotila, Zhengtong Yan, Jiaheng Lu (University of Helsinki), Maja Franz and Wolfgang Mauerer.

Abstract
In the last few years, the field of quantum computing has experienced remarkable progress. The prototypes of quantum computers already exist and have been made available to users through cloud services (e.g., IBM Q experience, Google quantum AI, or Xanadu quantum cloud). While fault-tolerant and large-scale quantum computers are not available yet (and may not be for a long time, if ever), the potential of this new technology is undeniable. Quantum algorithms have the proven ability to either outperform classical approaches for several tasks, or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even imperfect current-day technology is speculated to exhibit computational advantages over classical systems. Recent research is using quantum computers to solve machine learning tasks. Meanwhile, the database community already successfully applied various machine learning algorithms for data management tasks, so combining the fields seems to be a promising endeavour. However, quantum machine learning is a new research field for most database researchers. In this tutorial, we provide a fundamental introduction to quantum computing and quantum machine learning and show the potential benefits and applications for database research. In addition, we demonstrate how to apply quantum machine learning to the optimization of the join order problem for databases.

Contribution to the 26th International Conference on Computing in High Energy & Nuclear Physics (CHEP) by Maja Franz, Pia Zurita (University of Regensburg), Markus Diefenthaler (Jefferson Lab) and Wolfgang Mauerer.

Abstract
Quantum Computing (QC) is a promising early-stage technology that offers novel approaches to simulation and analysis in nuclear and high energy physics (NHEP). By basing computations directly on quantum mechanical phenomena, speedups and other advantages for many computationally hard tasks are potentially achievable, albeit both, the theoretical underpinning and the practical realization, are still subject to considerable scientific debate, which raises the question of applicability in NHEP.
In this contribution, we describe the current state of affairs in QC: Currently available noisy, intermediate-scale quantum (NISQ) computers suffer from a very limited number of quantum bits, and are subject to considerable imperfections, which narrows their practical computational capabilities. Our recent work on optimization problems suggests that the Co-Design of quantum hardware and algorithms is one route towards practical utility. This approach offers near-term advantages throughout a variety of domains, but requires interdisciplinary exchange between communities.
To this end, we identify possible classes of applications in NHEP, ranging from quantum process simulation over event classification directly at the quantum level to optimal real-time control of experiments. These types of algorithms are particularly suited for quantum algorithms that involve Variational Quantum Circuits, but might also benefit from more unusual special-purpose techniques like (Gaussian) Boson Sampling. We outline challenges and opportunities in the cross-domain cooperation between QC and NHEP, and show routes towards co-designed systems and algorithms. In particular, we aim at furthering the interdisciplinary exchange of ideas by establishing a joint understanding of requirements, limitations and possibilities.

Planung und Steuerung industrieller Fertigung: Quantum Learning Machine Atos QLM38 kommt im BMBF-Forschungsprojekt TAQO-PAM zum Einsatz.
Vorgezogenes Weihnachtsgeschenk für das Labor für Digitalisierung an der OTH Regensburg: Dort wurde jetzt eine Quantensimulationsanlage im Wert von einer Million Euro angeliefert und installiert. „Solche Hightech-Anlagen stehen üblicherweise in bedeutenden Instituten wie dem Forschungszentrum Jülich, dem Leibnitz Rechenzentrum München, bei der europäischen Organisation für Kernforschung (CERN) und im Munich Quantum Valley“, macht Präsident Prof. Dr. Ralph Schneider die besondere Dimension der Anschaffung deutlich.

Neue Professur im Rahmen der Hightech Agenda Bayern
Das Weihnachtsgeschenk kommt zwar optisch recht unscheinbar daher. Dennoch reiht sich die OTH Regensburg mit der Quantum Learning Machine „Atos QLM38“ ein in die Riege hochkarätiger Forschungsinstitute. Das kommt nicht von Ungefähr. An der Fakultät Informatik und Mathematik sind über Jahre hinweg Kompetenzen im Bereich Quantencomputing aufgebaut worden. Zuletzt hatte der Freistaat Bayern mitgeteilt, dass im Programm zur Stärkung von Quantenprofessuren im Rahmen der Hightech Agenda eine neue Professur für Algorithmik und Quantencomputing-Anwendungen an die OTH Regensburg geht.
Prof. Dr. Wolfgang Mauerer leitet das Labor für Digitalisierung und ist Vorsitzender Direktor des Regensburg Center for Artificial Intelligence (RCAI). Er beschäftigt sich seit mehr als 15 Jahren mit konkreten Anwendungsfällen der Quanteninformatik und gilt hierfür als ausgewiesener Experte. Ihm geht es nicht um den bloßen akademischen Austausch, sondern vor allem darum, die Lücke zwischen Grundlagenforschung und industrieller Anwendung zu schließen.

TAQO-PAM: Starke Partner aus Forschung und Industrie
Diesem Ziel widmet sich auch das von Mauerer ins Leben gerufene Konsortialprojekt TAQO-PAM, das über das Bundesministerium für Bildung und Forschung mit insgesamt 8,2 Millionen Euro gefördert wird. Dabei entfallen alleine 2,6 Millionen Euro auf die OTH Regensburg. Partner sind BMW München, Siemens München und Karlsruhe, die Regensburger Optware GmbH, die Friedrich-Alexander-Universität Erlangen-Nürnberg und Atos Scientific Computing (Tübingen).
Die zunehmende Massenproduktion individualisierter Güter und die dafür notwendige komplexe Logistik innerhalb moderner Fabriken erfordern die Lösung umfangreicher Optimierungsprobleme in Echtzeit. „Klassische Computer können solche Probleme nicht ausreichend gut und schnell verarbeiten; auch mit Quantencomputern ist die Machbarkeit nicht selbstverständlich“, bemerkt Mauerer. Im Projekt sollen unter seiner Führung daher hybride, quanten-klassische Spezialalgorithmen entworfen werden. Diese befähigen die demnächst verfügbaren Quantencomputer mit einigen 10 Qubits zur Lösung dieser Probleme beizutragen. Dies erfolgt durch die Integration von angepassten Quantenprozessoren (QPUs) in bestehende Szenarien und durch Erweiterung bestehender Methoden der Fabrikautomation und Produktionsplanung.

Stärkung des Hightech-Standorts Regensburg
"Das ist ein Paradebeispiel für die starke anwendungsorientierte und zukunftsgerichtete Forschung an unserer Hochschule", sind sich Präsident Schneider und Prof. Dr. Frank Herrmann, Dekan der Fakultät Informatik und Mathematik, einig. Sinnbildlich dafür steht das millionenschwere Weihnachtspaket mit dem Hochleistungsrechner. Ralph Schneider sieht darin auch eine Stärkung des Hightech-Standorts Regensburg. Längst nicht jede Hochschule und jede industrielle Forschungseinrichtung könne das nötige Fachwissen und die Ressourcen für die Nutzung einer Quantum Learning Machine bieten. In der Regel seien nicht nur Neueinsteiger in den Bereich des Quantencomputings auf einen teuren Zukauf von Rechenzeiten in großen Rechenzentren angewiesen.

Link zur Pressemitteilung der OTH Regensburg
©Fotos: OTH Regensburg/Michael Hitzek

At the Critical System Summit in Yokohama, Benno Bielmeier and Wolfgang Mauerer presented in one of six sessions a semiformal approach to deriving statements about the runtime behaviour of complex, mixed-criticality systems.
The presentation was recorded and can be found on YouTube.

As part of the Open Source Summit Japan, the event was hosted by the Linux Foundation and its corporate members, among them AT&T, Cisco, Fujitsu, Google, Hitachi, Huawei, IBM, Intel, Meta, Microsoft, NEC and many others, with more than 600 participants.

The approach links theoretical formalisms with empirically collected data from real-world applications and aims to remain interpretable and tangible. Its idea is to augment a simplified, formal model based on a priori knowledge about the system's intrinsics with empirical information from measurements on real-world scenarios, which then allows us to infer properties of interest for the certification of safety-critical systems.