@article{Schoenberger, author = {Sch{\"o}nberger, Manuel}, title = {Applicability of Quantum Computing on Database Query Optimization}, series = {SIGMOD '22: proceedings of the 2022 International Conference on Management of Data : June 12-17, 2022, Philadelphia, PA, USA}, journal = {SIGMOD '22: proceedings of the 2022 International Conference on Management of Data : June 12-17, 2022, Philadelphia, PA, USA}, publisher = {ACM}, address = {New York, NY}, doi = {10.1145/3514221.3520257}, pages = {2512 -- 2514}, abstract = {We evaluate the applicability of quantum computing on two fundamental query optimization problems, join order optimization and multi query optimization (MQO). We analyze the problem dimensions that can be solved on current gate-based quantum systems and quantum annealers, the two currently commercially available architectures. First, we evaluate the use of gate-based systems on MQO, previously solved with quantum annealing. We show that, contrary to classical computing, a different architecture requires involved adaptations. We moreover propose a multi-step reformulation for join ordering problems to make them solvable on current quantum systems. Finally, we systematically evaluate our contributions for gate-based quantum systems and quantum annealers. Doing so, we identify the scope of current limitations, as well as the future potential of quantum computing technologies for database systems.}, language = {en} } @misc{SchoenbergerScherzingerMauerer, author = {Sch{\"o}nberger, Manuel and Scherzinger, Stefanie and Mauerer, Wolfgang}, title = {Quantum Computing for DB - Applicability on Multi Query Optimization and Join Order Optimization}, series = {Fr{\"u}hjahrstreffen Fachgruppe Datenbanken in Potsdam, 2022}, journal = {Fr{\"u}hjahrstreffen Fachgruppe Datenbanken in Potsdam, 2022}, language = {en} } @inproceedings{SchoenbergerFranzScherzingeretal., author = {Sch{\"o}nberger, Manuel and Franz, Maja and Scherzinger, Stefanie and Mauerer, Wolfgang}, title = {Peel | Pile? Cross-Framework Portability of Quantum Software}, series = {2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C), 12-15 March 2022, Honolulu, HI, USA}, booktitle = {2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C), 12-15 March 2022, Honolulu, HI, USA}, publisher = {IEEE}, doi = {10.1109/ICSA-C54293.2022.00039}, abstract = {In recent years, various vendors have made quantum software frameworks available. Yet with vendor-specific frameworks, code portability seems at risk, especially in a field where hardware and software libraries have not yet reached a consolidated state, and even foundational aspects of the technologies are still in flux. Accordingly, the development of vendor-independent quantum programming languages and frameworks is often suggested. This follows the established architectural pattern of introducing additional levels of abstraction into software stacks, thereby piling on layers of abstraction. Yet software architecture also provides seemingly less abstract alternatives, namely to focus on hardware-specific formulations of problems that peel off unnecessary layers. In this article, we quantitatively and experimentally explore these strategic alternatives, and compare popular quantum frameworks from the software implementation perspective. We find that for several specific, yet generalisable problems, the mathematical formulation of the problem to be solved is not just sufficiently abstract and serves as precise description, but is likewise concrete enough to allow for deriving framework-specific implementations with little effort. Additionally, we argue, based on analysing dozens of existing quantum codes, that porting between frameworks is actually low-effort, since the quantum- and framework-specific portions are very manageable in terms of size, commonly in the order of mere hundreds of lines of code. Given the current state-of-the-art in quantum programming practice, this leads us to argue in favour of peeling off unnecessary abstraction levels.}, language = {en} } @unpublished{SchoenbergerTrummerMauerer, author = {Sch{\"o}nberger, Manuel and Trummer, Immanuel and Mauerer, Wolfgang}, title = {Quantum-Inspired Digital Annealing for Join Ordering}, series = {Proceedings of the VLDB Endowment}, journal = {Proceedings of the VLDB Endowment}, pages = {14}, 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.}, language = {en} } @unpublished{SchmidlDengMaetal., author = {Schmidl, Sebastian and Deng, Yangshen and Ma, Pingchuan and Sch{\"o}nberger, Manuel and Mauerer, Wolfgang}, title = {Reproducibility Report for ACM SIGMOD 2023 Paper: Ready to Leap (by Co-Design)? Join Order Optimisation}, abstract = {The paper "Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware" proposes the first approach to solve the problem of join order optimization on quantum hardware. The authors characterize the applicability and limitations of current state-of-the-art quantum hardware, i. e. gate-based quantum computing and quantum annealing, for join ordering and recommend key improvements to the physical hardware to reach practical utility. Based on the provided database queries and QPU system processing data, we have been able to reproduce the original paper's key insights and quantum problem characteristics reported in its experimental section. The authors provided a self-contained and fully automated reproduction package, including data (database queries, statistics, and collected QPU processing data), experiment scripts, and plotting routines that allowed the identical reconstruction of the three main figures in the paper.}, language = {en} } @misc{SchoenbergerScherzingerMauerer, author = {Sch{\"o}nberger, Manuel and Scherzinger, Stefanie and Mauerer, Wolfgang}, title = {Applicability of Quantum Computing on Database Query Optimization}, series = {Fr{\"u}hjahrstreffen Fachgruppe Datenbanken in Potsdam (Poster Presentation)}, journal = {Fr{\"u}hjahrstreffen Fachgruppe Datenbanken in Potsdam (Poster Presentation)}, language = {en} } @inproceedings{FranzWinkerGroppeetal., author = {Franz, Maja and Winker, Tobias and Groppe, Sven and Mauerer, Wolfgang}, title = {Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisation}, series = {2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 15-20 September 2024, Montreal, QC, Canada}, booktitle = {2024 IEEE International Conference on Quantum Computing and Engineering (QCE), 15-20 September 2024, Montreal, QC, Canada}, publisher = {IEEE}, doi = {10.1109/QCE60285.2024.00055}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-76877}, pages = {409 -- 420}, abstract = {Identifying optimal join orders (JOs) stands out as a key challenge in database research and engineering. Owing to the large search space, established classical methods rely on approximations and heuristics. Recent efforts have successfully explored reinforcement learning (RL) for JO. Likewise, quantum versions of RL have received considerable scientific attention. Yet, it is an open question if they can achieve sustainable, overall practical advantages with improved quantum processors. In this paper, we present a novel approach that uses quantum reinforcement learning (QRL) for JO based on a hybrid variational quantum ansatz. It is able to handle general bushy join trees instead of resorting to simpler left-deep variants as compared to approaches based on quantum(-inspired) optimisation, yet requires multiple orders of magnitudes fewer qubits, which is a scarce resource even for post-NISQ systems. Despite moderate circuit depth, the ansatz exceeds current NISQ capabilities, which requires an evaluation by numerical simulations. While QRL may not significantly outperform classical approaches in solving the JO problem with respect to result quality (albeit we see parity), we find a drastic reduction in required trainable parameters. This benefits practically relevant aspects ranging from shorter training times compared to classical RL, less involved classical optimisation passes, or better use of available training data, and fits data-stream and low-latency processing scenarios. Our comprehensive evaluation and careful discussion delivers a balanced perspective on possible practical quantum advantage, provides insights for future systemic approaches, and allows for quantitatively assessing trade-offs of quantum approaches for one of the most crucial problems of database management systems.}, language = {en} } @article{KruegerMauerer, author = {Kr{\"u}ger, Tom and Mauerer, Wolfgang}, title = {Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation}, series = {Quantum}, volume = {9}, journal = {Quantum}, publisher = {Verein zur F{\"o}rderung des Open Access Publizierens in den Quantenwissenschaften}, doi = {10.22331/q-2025-11-06-1903}, pages = {32}, abstract = {The Quantum Approximate Optimisation Algorithm (QAOA) is a widely studied quantum-classical iterative heuristic for combinatorial optimisation. While QAOA targets problems in complexity class NP, the classical optimisation procedure required in every iteration is itself known to be NP-hard. Still, advantage over classical approaches is suspected for certain scenarios, but nature and origin of its computational power are not yet satisfactorily understood. By introducing means of efficiently and accurately approximating the QAOA optimisation landscape from solution space structures, we derive a new algorithmic variant of unit-depth QAOA for two-level Hamiltonians (including all problems in NP): Instead of performing an iterative quantum-classical computation for each input instance, our non-iterative method is based on a quantum circuit that is instance-independent, but problem-specific. It matches or outperforms unit-depth QAOA for key combinatorial problems, despite reduced computational effort. Our approach is based on proving a long-standing conjecture regarding instance-independent structures in QAOA. By ensuring generality, we link existing empirical observations on QAOA parameter clustering to established approaches in theoretical computer science, and provide a sound foundation for understanding the link between structural properties of solution spaces and quantum optimisation.}, language = {de} } @inproceedings{SchoenbergerTrummerMauerer, author = {Sch{\"o}nberger, Manuel and Trummer, Immanuel and Mauerer, Wolfgang}, title = {Large-Scale Multiple Query Optimisation with Incremental Quantum(-Inspired) Annealing}, series = {Proceedings of the ACM on Management of Data}, volume = {3}, booktitle = {Proceedings of the ACM on Management of Data}, number = {4}, publisher = {ACM}, doi = {10.1145/3749171}, pages = {25}, abstract = {Multiple-query optimization (MQO) seeks to reduce redundant work across query batches. While MQO offers opportunities for dramatic performance improvements, the problem is NP-hard, limiting the sizes of problems that can be solved on generic hardware. We propose to leverage specialized hardware solvers for optimization, such as Fujitsu's Digital Annealer (DA), to scale up MQO to problem sizes formerly out of reach. We present a novel incremental processing approach that combines classical computation with DA acceleration. By efficiently partitioning MQO problems into sets of partial problems, and by applying a dynamic search steering strategy that reapplies initially discarded information to incrementally process individual problems, our method overcomes capacity limitations, and scales to extremely large MQO instances (up to νm1000 queries). A thorough and comprehensive empirical evaluation finds our method substantially outperforms existing approaches. Our generalisable framework lays the ground for other database use-cases on quantum-inspired hardware, and bridges towards future quantum accelerators.}, language = {en} } @unpublished{KruegerMauerer, author = {Kr{\"u}ger, Tom and Mauerer, Wolfgang}, title = {Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation}, pages = {20}, abstract = {The Quantum Approximate Optimisation Algorithm (qaoa) is a widely studied quantum-classical iterative heuristic for combinatorial optimisation. While qaoa targets problems in complexity class NP, the classical optimisation procedure required in every iteration is itself known to be NP-hard. Still, advantage over classical approaches is suspected for certain scenarios, but nature and origin of its computational power are not yet satisfactorily understood. By introducing means of efficiently and accurately approximating the qaoa optimisation landscape from solution space structures, we derive a new algorithmic variant: Instead of performing an iterative quantum-classical computation for each input instance, our non-iterative method is based on a quantum circuit that is instance-independent, but problem-specific. It matches or outperforms unit-depth qaoa for key combinatorial problems, despite reduced computational effort. Our approach is based on proving a long-standing conjecture regarding instance-independent structures in qaoa. By ensuring generality, we link existing empirical observations on qaoa parameter clustering to established approaches in theoretical computer science, and provide a sound foundation for understanding the link between structural properties of solution spaces and quantum optimisation.}, language = {en} } @inproceedings{RamsauerLohmannMauerer, author = {Ramsauer, Ralf and Lohmann, Daniel and Mauerer, Wolfgang}, title = {Reliable Pre-Integration Tracking of Commits on Mailing Lists}, series = {Invited Talk at Safety Summit at Embedded Linux Conference Europe (ELCE18)}, booktitle = {Invited Talk at Safety Summit at Embedded Linux Conference Europe (ELCE18)}, address = {Edinburgh, Scotland}, language = {en} } @inproceedings{SchoenbergerTrummerMauerer, author = {Sch{\"o}nberger, Manuel and Trummer, Immanuel and Mauerer, Wolfgang}, title = {Hybrid Mixed Integer Linear Programming for Large-Scale Join Order Optimisation}, series = {Proceedings of the VLDB Endowment}, volume = {19}, booktitle = {Proceedings of the VLDB Endowment}, number = {3}, publisher = {VLDB Endowment}, doi = {10.14778/3778092.3778097}, pages = {348 -- 360}, abstract = {Finding optimal join orders is among the most crucial steps to be performed by query optimisers. Though extensively studied in data management research, the problem remains far from solved: While query optimisers rely on exhaustive search methods to determine ideal solutions for small problems, such methods reach their limits once queries grow in size. Yet, large queries become increasingly common in real-world scenarios, and require suitable methods to generate efficient execution plans. While a variety of heuristics have been proposed for large-scale query optimisation, they suffer from degrading solution quality as queries grow in size, or feature highly sub-optimal worst-case behavior, as we will show. We propose a novel method based on the paradigm of mixed integer linear programming (MILP): By deriving a novel MILP model capable of optimising arbitrary bushy tree structures, we address the limitations of existing MILP methods for join ordering, and can rely on highly optimised MILP solvers to derive efficient tree structures that elude competing methods. To ensure optimisation efficiency, we embed our MILP method into a hybrid framework, which applies MILP solvers precisely where they provide the greatest advantage over competitors, while relying on more efficient methods for less complex optimisation steps. Thereby, our approach gracefully scales to extremely large query sizes joining up to 100 relations, and consistently achieves the most robust plan quality among a large variety of competing join ordering methods.}, language = {en} } @article{SchmidbauerLobeSchaeferetal., author = {Schmidbauer, Lukas and Lobe, Elisabeth and Schaefer, Ina and Mauerer, Wolfgang}, title = {It's Quick to be Square: Fast Quadratisation for Quantum Toolchains}, series = {ACM Transactions on Quantum Computing}, volume = {7}, journal = {ACM Transactions on Quantum Computing}, number = {2}, publisher = {ACM}, doi = {10.1145/3800943}, pages = {46}, abstract = {Many of the envisioned use-cases for quantum computers involve optimisation processes. While there are many algorithmic primitives to perform the required calculations, all eventually lead to quantum gates operating on quantum bits, with an order as determined by the structure of the objective function and the properties of target hardware. When the structure of the problem representation is not aligned with structure and boundary conditions of the executing hardware, various overheads degrading the computation may arise, possibly negating any possible quantum advantage. Therefore, automatic transformations of problem representations play an important role in quantum computing when descriptions (semi-)targeted at humans must be cast into forms that can be "executed" on quantum computers. Mathematically equivalent formulations are known to result in substantially different non-functional properties depending on hardware, algorithm and detail properties of the problem. Given the current state of noisy intermediate-scale quantum (NISQ) hardware, these effects are considerably more pronounced than in classical computing. Likewise, efficiency of the transformation itself is relevant because possible quantum advantage may easily be eradicated by the overhead of transforming between representations. In this paper, we consider a specific class of higher-level representations, that is, PUBOs, and devise novel automatic transformation mechanisms into widely used QUBOs that substantially improve efficiency and versatility over the state of the art. In addition, we conduct a comprehensive investigation of industry-relevant problem formulations and their conversion into a quantum-specific representation, identifying significant obstacles in scaling behaviour and demonstrating how these can be circumvented.}, language = {en} } @unpublished{RamsauerMauerer, author = {Ramsauer, Ralf and Mauerer, Wolfgang}, title = {Towards System-Level Quantum-Accelerator Integration}, doi = {10.48550/arXiv.2507.19212}, pages = {6}, abstract = {Quantum computers are often treated as experimental add-ons that are loosely coupled to classical infrastructure through high-level interpreted languages and cloud-like orchestration. However, future deployments in both, high-performance computing (HPC) and embedded environments, will demand tighter integration for lower latencies, stronger determinism, and architectural consistency, as well as to implement error correction and other tasks that require tight quantum-classical interaction as generically as possible. We propose a vertically integrated quantum systems architecture that treats quantum accelerators and processing units as peripheral system components. A central element is the Quantum Abstraction Layer (QAL) at operating system kernel level. It aims at real-time, low-latency, and high-throughput interaction between quantum and classical resources, as well as robust low-level quantum operations scheduling and generic resource management. It can serve as blueprint for orchestration of low-level computational components "around" a QPU (and inside a quantum computer), and across different modalities. We present first results towards such an integrated architecture, including a virtual QPU model based on QEMU. The architecture is validated through functional emulation on three base architectures (x86_64, ARM64, and RISC-V), and timing-accurate FPGA-based simulations. This allows for a realistic evaluation of hybrid system performance and quantum advantage scenarios. Our work lays the ground for a system-level co-design methodology tailored for the next generation of quantum-classical computing.}, language = {en} } @unpublished{SchmidbauerLobeSchaeferetal., author = {Schmidbauer, Lukas and Lobe, Elisabeth and Sch{\"a}fer, Ina and Mauerer, Wolfgang}, title = {It's Quick to be Square: Fast Quadratisation for Quantum Toolchains}, abstract = {Many of the envisioned use-cases for quantum computers involve optimisation processes. While there are many algorithmic primitives to perform the required calculations, all eventually lead to quantum gates operating on quantum bits, with an order as determined by the structure of the objective function and the properties of target hardware. When the structure of the problem representation is not aligned with structure and boundary conditions of the executing hardware, various overheads degrading the computation may arise, possibly negating any possible quantum advantage. Therefore, automatic transformations of problem representations play an important role in quantum computing when descriptions (semi-)targeted at humans must be cast into forms that can be executed on quantum computers. Mathematically equivalent formulations are known to result in substantially different non-functional properties depending on hardware, algorithm and detail properties of the problem. Given the current state of noisy intermediate-scale quantum (NISQ) hardware, these effects are considerably more pronounced than in classical computing. Likewise, efficiency of the transformation itself is relevant because possible quantum advantage may easily be eradicated by the overhead of transforming between representations. In this paper, we consider a specific class of higher-level representations, i.e. polynomial unconstrained binary optimisation problems, and devise novel automatic transformation mechanisms into widely used quadratic unconstrained binary optimisation problems that substantially improve efficiency and versatility over the state of the art. We also identify what influence factors of lower-level details can be abstracted away in the transformation process, and which details must be made available to higher-level abstractions.}, language = {de} } @inproceedings{SafiNiedermeierMauerer, author = {Safi, Hila and Niedermeier, Christoph and Mauerer, Wolfgang}, title = {TWiDDle: Twirling and Dynamical Decoupling, and Crosstalk Noise Modeling}, series = {2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 30 August - 05 September 2025, Albuquerque}, booktitle = {2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 30 August - 05 September 2025, Albuquerque}, publisher = {IEEE}, doi = {10.1109/QCE65121.2025.10313}, pages = {162 -- 168}, abstract = {Crosstalk remains a major source of correlated error in quantum systems, yet lacks a precise, community-wide definition - hindering systematic analysis and mitigation. This paper introduces a model-driven approach to crosstalk characterisation through three architecture-inspired noise models: (1) simultaneous two-qubit gate execution, (2) shared qubit interference, and (3) proximity-induced noise from shared control or readout hardware. These models act as both diagnostic tools and building blocks for crosstalk-aware quantum programming. We assess their impact across a broad benchmark suite - quantum simulation, Grover's algorithm, and fault-tolerant primitives like surface, Shor, and Steane codes - and evaluate two mitigation techniques: dynamical decoupling and Pauli twirling. While both are discussed in literature, only dynamical decoupling consistently enhances fidelity across noise types. Our work links low-level noise effects to high-level software engineering, underscoring the role of hardware-software co-design in scalable quantum computing. Model-based, hardware-aware design flows and composable noise abstractions improve error mitigation and program portability. Integrating such strategies into the toolchain is essential for building resilient quantum programs under realistic noise conditions.}, language = {en} } @unpublished{PeriyasamyUfrechtSchereretal., author = {Periyasamy, Maniraman and Ufrecht, Christian and Scherer, Daniel D. D. and Mauerer, Wolfgang}, title = {CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cutting}, doi = {10.48550/arXiv.2506.14858}, pages = {4}, abstract = {Whether QML can offer a transformative advantage remains an open question. The severe constraints of NISQ hardware, particularly in circuit depth and connectivity, hinder both the validation of quantum advantage and the empirical investigation of major obstacles like barren plateaus. Circuit cutting techniques have emerged as a strategy to execute larger quantum circuits on smaller, less connected hardware by dividing them into subcircuits. However, this partitioning increases the number of samples needed to estimate the expectation value accurately through classical post-processing compared to estimating it directly from the full circuit. This work introduces a novel regularization term into the QML optimization process, directly penalizing the overhead associated with sampling. We demonstrate that this approach enables the optimizer to balance the advantages of gate cutting against the optimization of the typical ML cost function. Specifically, it navigates the trade-off between minimizing the cutting overhead and maintaining the overall accuracy of the QML model, paving the way to study larger complex problems in pursuit of quantum advantage}, language = {en} } @article{MaschekSchwittallaFranzetal., author = {Maschek, Stefan Raimund and Schwittalla, J{\"u}rgen and Franz, Maja and Mauerer, Wolfgang}, title = {Make Some Noise! Measuring Noise Model Quality in Real-World Quantum Software}, series = {Proceedings of the IEEE International Conference on Quantum Software (QSW), 07-12 July 2025, Helsinki}, journal = {Proceedings of the IEEE International Conference on Quantum Software (QSW), 07-12 July 2025, Helsinki}, publisher = {IEEE}, doi = {10.1109/QSW67625.2025.00010}, pages = {11}, abstract = {Noise and imperfections are among the prevalent challenges in quantum software engineering for current NISQ systems. They will remain important in the post-NISQ area, as logical, error-corrected qubits will be based on software mechanisms. As real quantum hardware is still limited in size and accessibility, noise models for classical simulation-that in some cases can exceed dimensions of actual systems-play a critical role in obtaining insights into quantum algorithm performance, and the properties of mechanisms for error correction and mitigation. We present, implement and validate a tunable noise model building on the Kraus channel formalism on a large scale quantum simulator system (Qaptiva). We use empirical noise measurements from IBM quantum (IBMQ) systems to calibrate the model and create a realistic simulation environment. Experimental evaluation of our approach with Greenberger-Horne-Zeilinger (GHZ) state preparation and QAOA applied to an industrial usecase validate our approach, and demonstrate accurate simulation of hardware behaviour at reasonable computational cost. We devise and utilise a method that allows for determining the quality of noise models for larger problem instances than is possible with existing metrics in the literature. To identify potentials of future quantum software and algorithms, we extrapolate the noise model to future partially fault-tolerant systems, and give insights into the interplay between hardware-specific noise modelling and hardware-aware algorithm development.}, language = {en} } @unpublished{FranzStroblChaichenetsetal., author = {Franz, Maja and Strobl, Melvin and Chaichenets, Leonid and K{\"u}hn, Eileen and Streit, Achim and Mauerer, Wolfgang}, title = {Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models}, doi = {10.48550/arXiv.2506.09527}, pages = {32}, abstract = {Variational quantum algorithms have received substantial theoretical and empirical attention. As the underlying variational quantum circuit (VQC) can be represented by Fourier series that contain an exponentially large spectrum in the number of input features, hope for quantum advantage remains. Nevertheless, it remains an open problem if and how quantum Fourier models (QFMs) can concretely outperform classical alternatives, as the eventual sources of non-classical computational power (for instance, the role of entanglement) are far from being fully understood. Likewise, hardware noise continues to pose a challenge that will persist also along the path towards fault tolerant quantum computers. In this work, we study VQCs with Fourier lenses, which provides possibilities to improve their understanding, while also illuminating and quantifying constraints and challenges. We seek to elucidate critical characteristics of QFMs under the influence of noise. Specifically, we undertake a systematic investigation into the impact of noise on the Fourier spectrum, expressibility, and entangling capability of QFMs through extensive numerical simulations and link these properties to training performance. The insights may inform more efficient utilisation of quantum hardware and support the design of tailored error mitigation and correction strategies. Decoherence imparts an expected and broad detrimental influence across all Ans{\"a}tze. Nonetheless, we observe that the severity of these deleterious effects varies among different model architectures, suggesting that certain configurations may exhibit enhanced robustness to noise and show computational utility.}, language = {en} } @article{FranzSchoenbergerStrobletal., author = {Franz, Maja and Sch{\"o}nberger, Manuel and Strobl, Melvin and K{\"u}hn, Eileen and Streit, Achim and Zurita, P{\´i}a and Diefenthaler, Markus and Mauerer, Wolfgang}, title = {From Hope to Heuristic: Realistic Runtime Estimates for Quantum Optimisation in NHEP}, series = {EPJ Web of Conferences; 27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)}, volume = {337}, journal = {EPJ Web of Conferences; 27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)}, publisher = {EDP Sciences}, doi = {10.1051/epjconf/202533701282}, pages = {8}, abstract = {Noisy Intermediate-Scale Quantum (NISQ) computers, despite their limitations, present opportunities for near-term quantum advantages in Nuclear and High-Energy Physics (NHEP) when paired with specially designed quantum algorithms and processing units. This study focuses on core algorithms that solve optimization problems through the quadratic Ising or Quadratic Unconstrained Binary Optimisation model, specifically Quantum Annealing and the Quantum Approximate Optimisation Algorithm (QAOA). In particular, we estimate runtimes and scalability for the task of particle Track Reconstruction (TR), a key computing challenge in NHEP, and investigate how the classical parameter space in QAOA, along with techniques like a Fourieranalysis based heuristic, can facilitate future quantum advantages. The findings indicate that lower frequency components in the parameter space are crucial for effective annealing schedules, suggesting that heuristics can improve resource efficiency while achieving near-optimal results. Overall, the study highlights the potential of NISQ computers in NHEP and the significance of co-design approaches and heuristic techniques in overcoming challenges in quantum algorithms.}, language = {en} } @inproceedings{SchmidbauerMauerer, author = {Schmidbauer, Lukas and Mauerer, Wolfgang}, title = {SAT Strikes Back: Parameter and Path Relations in Quantum Toolchains}, series = {Proceedings of the IEEE International Conference on Quantum Software (QSW), 07-12 July 2025, Helsinki}, booktitle = {Proceedings of the IEEE International Conference on Quantum Software (QSW), 07-12 July 2025, Helsinki}, publisher = {IEEE}, doi = {10.1109/QSW67625.2025.00021}, pages = {104 -- 115}, abstract = {In the foreseeable future, toolchains for quantum computing should offer automatic means of transforming a high level problem formulation down to a hardware executable form. Thereby, it is crucial to find (multiple) transformation paths that are optimised for (hardware specific) metrics. We zoom into this pictured tree of transformations by focussing on k-SAT instances as input and their transformation to QUBO, while considering structure and characteristic metrics of input, intermediate and output representations. Our results can be used to rate valid paths of transformation in advance—also in automated (quantum) toolchains. We support the automation aspect by considering stability and therefore predictability of free parameters and transformation paths. Moreover, our findings can be used in the manifesting era of error correction (since considering structure in a high abstraction layer can benefit error correcting codes in layers below). We also show that current research is closely linked to quadratisation techniques and their mathematical foundation.}, language = {en} } @inproceedings{SchmidbauerRiofrioHeinrichetal., author = {Schmidbauer, Lukas and Riofr{\´i}o, Carlos A. and Heinrich, Florian and Junk, Vanessa and Schwenk, Ulrich and Husslein, Thomas and Mauerer, Wolfgang}, title = {Path Matters: Industrial Data Meet Quantum Optimization}, series = {2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 30 August - 05 September 2025, Albuquerque}, booktitle = {2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 30 August - 05 September 2025, Albuquerque}, publisher = {IEEE}, doi = {10.1109/QCE65121.2025.00230}, pages = {2101 -- 2111}, abstract = {Real-world optimization problems must undergo a series of transformations before becoming solvable on current quantum hardware. Even for a fixed problem, the number of possible transformation paths-from industry-relevant formulations through binary constrained linear programs (BILPs), to quadratic unconstrained binary optimization (QUBO), and finally to a hardware-executable representation-is remarkably large. Each step introduces free parameters, such as Lagrange multipliers, encoding strategies, slack variables, rounding schemes or algorithmic choices-making brute-force exploration of all paths intractable. In this work, we benchmark a representative subset of these transformation paths using a realworld industrial production planning problem with industry data: the optimization of work allocation in a press shop producing vehicle parts. We focus on QUBO reformulations and algorithmic parameters for both quantum annealing (QA) and the Linear Ramp Quantum Approximate Optimization Algorithm (LR-QAOA). Our goal is to identify a reduced set of effective configurations applicable to similar industrial settings. Our results show that QA on D-Wave hardware consistently produces near-optimal solutions, whereas LR-QAOA on IBM quantum devices struggles to reach comparable performance. Hence, the choice of hardware and solver strategy significantly impacts performance. The problem formulation and especially the penalization strategy determine the solution quality. Most importantly, mathematically-defined penalization strategies are equally successful as hand-picked penalty factors, paving the way for automated QUBO formulation. Moreover, we observe a strong correlation between simulated and quantum annealing performance metrics, offering a scalable proxy for predicting QA behavior on larger problem instances.}, language = {en} } @inproceedings{BielmeierRamsauerYoshidaetal., author = {Bielmeier, Benno and Ramsauer, Ralf and Yoshida, Takahiro and Mauerer, Wolfgang}, title = {From Tracepoints to Timeliness: a Semi-Markov Framework for Predictive Runtime Analysis}, series = {IEEE 31th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), 20-22 August 2025, Singapore}, booktitle = {IEEE 31th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), 20-22 August 2025, Singapore}, publisher = {IEEE}, doi = {10.1109/RTCSA66114.2025.00021}, pages = {114 -- 125}, abstract = {Detecting and resolving violations of temporal constraints in real-time systems is both, time-consuming and resource-intensive, particularly in complex software environments. Measurement-based approaches are widely used during development, but often are unable to deliver reliable predictions with limited data. This paper presents a hybrid method for worst-case execution time estimation, combining lightweight runtime tracing with probabilistic modelling. Timestamped system events are used to construct a semi-Markov chain, where transitions represent empirically observed timing between events. Execution duration is interpreted as time-to-absorption in the semi-Markov chain, enabling worst-case execution time estimation with fewer assumptions and reduced overhead. Empirical results from real-time Linux systems indicate that the method captures both regular and extreme timing behaviours accurately, even from short observation periods. The model supports holistic, low-intrusion analysis across system layers and remains interpretable and adaptable for practical use.}, language = {en} } @article{FeilnerRamsauer, author = {Feilner, Markus and Ramsauer, Ralf}, title = {Open-Source-Wissenschaftler bereiten Software f{\"u}r das Quantencomputing vor}, series = {Linux-Magazin}, volume = {25}, journal = {Linux-Magazin}, number = {08}, publisher = {Deloitte}, pages = {7}, abstract = {Um Software und Architekturen f{\"u}r das Quantencomputing zu entwickeln, zu definieren und zu testen, braucht man keinen Quantencomputer. Weil die teuer und rar sind, entschieden sich Wissenschaftler der OTH Regensburg daf{\"u}r, sie einfach zu virtualisieren - mit messbarem Erfolg.}, language = {en} } @inproceedings{ThelenMauerer, author = {Thelen, Simon and Mauerer, Wolfgang}, title = {Predict and Conquer: Navigating Algorithm Trade-Offs with Quantum Design Automation}, series = {2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 30 August - 05 September 2025, Albuquerque}, booktitle = {2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 30 August - 05 September 2025, Albuquerque}, publisher = {IEEE}, address = {Los Alamitos, USA}, doi = {10.1109/QCE65121.2025.00071}, pages = {591 -- 602}, abstract = {Combining quantum computers with classical compute power has become a standard means for developing algorithms and heuristics that are, eventually, supposed to beat any purely classical alternatives. While in-principle advantages for solution quality or runtime are expected for increasingly many approaches, substantial challenges remain: Non-functional properties like runtime or solution quality of many suggested approaches are not yet fully understood, and need to be explored empirically. This, in turn, makes it unclear which approach is best suited for a given problem. Accurately predicting behaviour and properties of quantum-classical algorithms opens possibilities for software abstraction layers, which in turn can automate decisionmaking for algorithm selection and parametrisation. While such techniques find frequent use in classical high-performance computing, they are still mostly absent from quantum software toolchains. In this paper, we present a methodology (accompanied by a reproducible reference implementation) to perform algorithm selection based on desirable non-functional requirements. This greatly simplifies decision-making processes for end users. Based on meta-information annotations at the source code level, our framework traces key characteristics of quantum-classical heuristics and algorithms, and uses this information to predict the most suitable approach and its parameters for given computational challenges and their non-functional requirements. As combinatorial optimisation is a very extensively studied aspect of quantumclassical systems, we perform a comprehensive case study based on numerical simulations of algorithmic approaches to implement and validate our ideas. We develop statistical models to quantify the influence of various factors on non-functional properties, and establish predictions for optimal algorithmic choices without manual user effort. We argue that our methodology generalises to problem classes beyond combinatorial optimisation, such as Hamiltonian optimisation, and lays a foundation for integrated software layers for quantum design automation.}, language = {en} }