TY - CHAP A1 - Mauerer, Wolfgang A1 - Klessinger, Stefan A1 - Scherzinger, Stefanie T1 - Beyond the badge: reproducibility engineering as a lifetime skill T2 - Proceedings 4th International Workshop on Software Engineering Education for the Next Generation SEENG 2022, 17 May 2022, Pittsburgh, PA, USA N2 - Ascertaining reproducibility of scientific experiments is receiving increased attention across disciplines. We argue that the necessary skills are important beyond pure scientific utility, and that they should be taught as part of software engineering (SWE) education. They serve a dual purpose: Apart from acquiring the coveted badges assigned to reproducible research, reproducibility engineering is a lifetime skill for a professional industrial career in computer science. SWE curricula seem an ideal fit for conveying such capabilities, yet they require some extensions, especially given that even at flagship conferences like ICSE, only slightly more than one-third of the technical papers (at the 2021 edition) receive recognition for artefact reusability. Knowledge and capabilities in setting up engineering environments that allow for reproducing artefacts and results over decades (a standard requirement in many traditional engineering disciplines), writing semi-literate commit messages that document crucial steps of a decision-making process and that are tightly coupled with code, or sustainably taming dynamic, quickly changing software dependencies, to name a few: They all contribute to solving the scientific reproducibility crisis, and enable software engineers to build sustainable, long-term maintainable, software-intensive, industrial systems. We propose to teach these skills at the undergraduate level, on par with traditional SWE topics. KW - reproducibility engineering KW - teaching software engineering Y1 - 2022 SN - 9781450393362 U6 - https://doi.org/10.1145/3528231.3528359 N1 - Preprint unter: https://doi.org/10.48550/arXiv.2203.05283 SP - 1 EP - 4 PB - ACM CY - New York, NY, USA ER - TY - CHAP A1 - Winker, Tobias A1 - Groppe, Sven A1 - Uotila, Valter Johan Edvard A1 - Yan, Zhengtong A1 - Lu, Jiaheng A1 - Maja, Franz A1 - Mauerer, Wolfgang T1 - Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research T2 - SIGMOD '23, proceedings of the 2023 International Conference on Management of Data: June 18-23, 2023, Seattle, WA, USA N2 - 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 havethe 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 join order problem for databases. Y1 - 2023 U6 - https://doi.org/10.1145/3555041.3589404 PB - ACM CY - New York ER - TY - INPR A1 - Thelen, Simon A1 - Safi, Hila A1 - Mauerer, Wolfgang T1 - Approximating under the Influence of Quantum Noise and Compute Power T2 - Proceedings of WIHPQC@IEEE QCE N2 - The quantum approximate optimisation algorithm (QAOA) is at the core of many scenarios that aim to combine the power of quantum computers and classical high-performance computing appliances for combinatorial optimisation. Several obstacles challenge concrete benefits now and in the foreseeable future: Imperfections quickly degrade algorithmic performance below practical utility; overheads arising from alternating between classical and quantum primitives can counter any advantage; and the choice of parameters or algorithmic variant can substantially influence runtime and result quality. Selecting the optimal combination is a non-trivial issue, as it not only depends on user requirements, but also on details of the hardware and software stack. Appropriate automation can lift the burden of choosing optimal combinations for end-users: They should not be required to understand technicalities like differences between QAOA variants, required number of QAOA layers, or necessary measurement samples. Yet, they should receive best-possible satisfaction of their non-functional requirements, be it performance or other. We determine factors that affect solution quality and temporal behaviour of four QAOA variants using comprehensive density-matrix-based simulations targeting three widely studied optimisation problems. Our simulations consider ideal quantum computation, and a continuum of scenarios troubled by realistic imperfections. Our quantitative results, accompanied by a comprehensive reproduction package, show strong differences between QAOA variants that can be pinpointed to narrow and specific effects. We identify influential co-variables and relevant non-functional quality goals that, we argue, mark the relevant ingredients for designing appropriate software engineering abstraction mechanisms and automated tool-chains for devising quantum solutions from high-level problem specifications. Y1 - 2024 ER - TY - INPR A1 - Periyasamy, Maniraman A1 - Plinge, Axel A1 - Mutschler, Christopher A1 - Scherer, Daniel D. A1 - Mauerer, Wolfgang T1 - Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule N2 - The study of variational quantum algorithms (VQCs) has received significant attention from the quantum computing community in recent years. These hybrid algorithms, utilizing both classical and quantum components, are well-suited for noisy intermediate-scale quantum devices. Though estimating exact gradients using the parameter-shift rule to optimize the VQCs is realizable in NISQ devices, they do not scale well for larger problem sizes. The computational complexity, in terms of the number of circuit evaluations required for gradient estimation by the parameter-shift rule, scales linearly with the number of parameters in VQCs. On the other hand, techniques that approximate the gradients of the VQCs, such as the simultaneous perturbation stochastic approximation (SPSA), do not scale with the number of parameters but struggle with instability and often attain suboptimal solutions. In this work, we introduce a novel gradient estimation approach called Guided-SPSA, which meaningfully combines the parameter-shift rule and SPSA-based gradient approximation. The Guided-SPSA results in a 15% to 25% reduction in the number of circuit evaluations required during training for a similar or better optimality of the solution found compared to the parameter-shift rule. The Guided-SPSA outperforms standard SPSA in all scenarios and outperforms the parameter-shift rule in scenarios such as suboptimal initialization of the parameters. We demonstrate numerically the performance of Guided-SPSA on different paradigms of quantum machine learning, such as regression, classification, and reinforcement learning. Y1 - 2024 ER - TY - CHAP A1 - Zwingel, Maximilian A1 - Kedilioglu, Oguz A1 - Reitelshöfer, Sebastian A1 - Mauerer, Wolfgang T1 - Optimization Problems in Production and Planning: Approaches and Limitations in View of Possible Quantum Superiority T2 - Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2023 Y1 - 2023 SN - 9783031740091 PB - Springer Nature ER - TY - CHAP A1 - Tresp, Volker A1 - Udluft, Steffen A1 - Hein, Daniel A1 - Hauptmann, Werner A1 - Leib, Martin A1 - Mutschler, Christopher A1 - Scherer, Daniel D. A1 - Mauerer, Wolfgang T1 - Workshop Summary: Quantum Machine Learning T2 - 2023 IEEE International Conference on Quantum Computing and Engineering, Bellevue, WA, United States, September 17-22, 2023 Y1 - 2023 U6 - https://doi.org/10.1109/QCE57702.2023.10174 PB - IEEE ER - TY - CHAP A1 - Gogeißl, Martin A1 - Safi, Hila A1 - Mauerer, Wolfgang T1 - Quantum Data Encoding Patterns and their Consequences T2 - Q-Data '24: Proceedings of the 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications, June 9 - 15, 2024, Santiago AA Chile N2 - The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, in particular for discrete optimisation. While a few groundbreaking algorithmic approaches are known that can provably outperform classical computers, we observe a scarcity of programming abstractions for constructing efficient quantum algorithms. A good fraction of the literature that addresses solving concrete problems related to database management concentrates on casting them as quadratic unconstrained binary optimisation problems (QUBOs), which can then, among others, be processed on gate-based machines (using the quantum approximate optimisation algorithm), or quantum annealers. A critical aspect that affects efficiency and scalability of either of these approaches is how classical data are loaded into qubits, respectively how problems are encoded into QUBO representation. The effectiveness of encodings is known to be of crucial importance for quantum computers, especially since the amount of available qubits is strongly limited in the era of noisy, intermediate-size quantum computers. In this paper, we present three encoding patterns, discuss their impact on scalability, and their ease of use. We consider the recreational (yet computationally challenging) Sudoku problem and its reduction to graph colouring as an illustrative example to discuss their individual benefits and disadvantages. Our aim is enable database researchers to choose an appropriate encoding scheme for their purpose without having to acquire in-depth knowledge on quantum peculiarities, thus easing the path towards applying quantum acceleration on data management systems. Y1 - 2024 SN - 979-8-4007-0553-3 U6 - https://doi.org/10.1145/3665225.3665446 SP - 27 EP - 37 PB - ACM ER - TY - INPR A1 - Jung, Matthias A1 - Krumke, Sven O. A1 - Schroth, Christof A1 - Lobe, Elisabeth A1 - Mauerer, Wolfgang T1 - QCEDA: Using Quantum Computers for EDA N2 - The field of Electronic Design Automation (EDA) is crucial for microelectronics, but the increasing complexity of Integrated Circuits (ICs) poses challenges for conventional EDA: Corresponding problems are often NP-hard and are therefore in general solved by heuristics, not guaranteeing optimal solutions. Quantum computers may offer better solutions due to their potential for optimization through entanglement, superposition, and interference. Most of the works in the area of EDA and quantum computers focus on how to use EDA for building quantum circuits. However, almost no research focuses on exploiting quantum computers for solving EDA problems. Therefore, this paper investigates the feasibility and potential of quantum computing for a typical EDA optimization problem broken down to the Min-k-Union problem. The problem is mathematically transformed into a Quadratic Unconstrained Binary Optimization (QUBO) problem, which was successfully solved on an IBM quantum computer and a D-Wave quantum annealer. Y1 - 2024 ER - TY - CHAP A1 - Mauerer, Wolfgang ED - Exman, Iaakov ED - Perez-Castillo, Ricardo ED - Piattini, Mario ED - Felderer, Michael T1 - Superoperators for Quantum Software Engineering T2 - Quantum Software: Aspects of Theory and System Design N2 - As implementations of quantum computers grow in size and maturity, the question of how to program this new class of machines is attracting increasing attention in the software engineering domain. Yet, many questions from how to design expressible quantum languages augmented with formal semantics via implementing appropriate optimizing compilers to abstracting details of machine properties in software systems remain challenging. Performing research at this intersection of quantum computing and software engineering requires sufficient knowledge of the physical processes underlying quantum computations, and how to model these. In this chapter, we review a superoperator-based approach to quantum dynamics, as it can provide means that are sufficiently abstract, yet concrete enough to be useful in quantum software and systems engineering, and outline how it is used in several important applications in the field. KW - quantum computing KW - quantum software engineering KW - software development KW - software engineering Y1 - 2024 U6 - https://doi.org/10.1007/978-3-031-64136-7_3 SN - 978-3-031-64136-7 PB - Springer Nature ER - TY - CHAP A1 - Safi, Hila A1 - Wintersperger, Karen A1 - Mauerer, Wolfgang T1 - Influence of HW-SW-Co-Design on Quantum Computing Scalability T2 - 2023 IEEE International Conference on Quantum Software (QSW), Chicago, IL, USA, 02-08 July 2023 N2 - The use of quantum processing units (QPUs) promises speed-ups for solving computational problems. Yet, current devices are limited by the number of qubits and suffer from significant imperfections, which prevents achieving quantum advantage. To step towards practical utility, one approach is to apply hardware-software co-design methods. This can involve tailoring problem formulations and algorithms to the quantum execution environment, but also entails the possibility of adapting physical properties of the QPU to specific applications. In this work, we follow the latter path, and investigate how key figures— circuit depth and gate count—required to solve four cornerstone NP-complete problems vary with tailored hardware properties. Our results reveal that achieving near-optimal performance and properties does not necessarily require optimal quantum hardware, but can be satisfied with much simpler structures that can potentially be realised for many hardware approaches.m Using statistical analysis techniques, we additionally identify an underlying general model that applies to all subject problems. This suggests that our results may be universally applicable to other algorithms and problem domains, and tailored QPUs can find utility outside their initially envisaged problem domains. The substantial possible improvements nonetheless highlight the importance of QPU tailoring to progress towards practical deployment and scalability of quantum software. KW - quantum computing KW - software engineering KW - hardware-software co-design KW - quantum algorithm performance analysis KW - scalability of quantum applications Y1 - 2023 SN - 979-8-3503-0479-4 U6 - https://doi.org/10.1109/QSW59989.2023.00022 SP - 104 EP - 115 PB - IEEE ER -