Laboratory for Digitalisation — Prof. Dr. Wolfgang Mauerer

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.

Tom Krüger has joined the team as doctoral student in the field of quantum computing, contributing to the TAQO-PAM project. Welcome, Tom!

Wolfgang Mauerer, Ralf Ramsauer and Andrej Utz present results of the iDev 4.0 project at the SEMICON Europa 2022 in Munich.

Abstract: The advent of multi-core CPUs in nearly all embedded markets has prompted an architectural trend towards combining safety critical and uncritical software on single hardware units. We present an architecture for mixed-criticality systems based on Linux that allows for the consolidation of critical and uncritical components onto a single hardware unit.

In the context of the iDev 4.0 project, the use-case of this technological building block is to reduce the overall amount of distributed computational hardware components accross semiconductor assembly lines in fabs.

CPU virtualisation extensions enable strict and static partitioning of hardware by direct assignment of resources, which allows us to boot additional operating systems or bare metal applications running aside Linux. The hypervisor Jailhouse is at the core of the architecture and ensures that the resulting domains may serve workloads of different criticality and can not interfere in an unintended way. This retains Linux’s feature-richness in uncritical parts, while frugal safety and real-time critical applications execute in isolated domains. Architectural simplicity is a central aspect of our approach and a precondition for reliable implementability and successful certification.

In this work, we present our envisioned base system architecture, and elaborate implications on the transition from existing legacy systems to a consolidated environment.

Contribution to CORE A* conference ACM SIGMOD driven by Manuel Schönberger and Wolfgang Mauerer breaks new ground for quantum computing in the database community (PDF).

Abstract: The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for the known quantum algorithms. We present the first quantum implementation of join ordering, which is one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes that can be processed with current quantum annealers. However, we also confirm that limits of early-stage technology are quickly reached. Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future quantum computers, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and possible problem size. In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.

Felix Greiwe has joined the group as doctoral student in the field of quantum computing, contributing to the TAQO-PAM project. Welcome, Felix!

Contribution to the highly competitive Open Source Summit (with acceptance rates below 20%) in Yokohama, Japan by Benno Bielmeier and Wolfgang Mauerer.

Abstract: Software for safety-critical systems must meet strict functional and temporal requirements. Since it is impossible to exhaustively test the required qualities, formal verification techniques have been devised. However, these approaches are usually only applicable to small systems, and require software architecture and development to consider verification goals from the ground up. Safety-critical systems face an increasing demand for functionality, and need to handle the associated complexity. While the desired functionalities can be satisfied by embedded Linux, established verification techniques fail for code of such magnitude. We show a semi-formal, model-based approach to derive reliable statements about the run-time characteristic of embedded Linux. Using a-priori expert knowledge, we generate a finite automaton-based effective description of safety-relevant aspects. The real-world, system-dependent behaviour of the resulting automata, in particular timing statistics for state transitions, is empirically obtained via system instrumentation. We then show how to turn this into (statistical) guarantees on their behaviour. We show how this allows to draw conclusions that can be used in certifying systems in terms of reliability, latencies, and other real-time properties.