arXiv subjects

Wolfgang Mauerer

Publications and source records attributed to Wolfgang Mauerer.

At least 37 records · Page 2Linked to original sources

arXiv2026

Towards Quantum Software for Quantum Simulation

Quantum simulation is a leading candidate for demonstrating practical quantum advantage over classical computation, as it is believed to provide exponentially more compute power than any classical system. It offers new means of studying the behaviour of complex physical systems, for which conventionally software-intensive simulation codes based on numerical high-performance computing are used. Instead, quantum simulations map properties and characteristics of subject systems, for instance chemical molecules, onto quantum devices that then mimic the system under study. Currently, the use of these techniques is largely limited to fundamental science, as the overall approach remains tailored for specific problems: We lack infrastructure and modelling abstractions that are provided by the software engineering community for other computational domains. In this paper, we identify critical gaps in the quantum simulation software stack-particularly the absence of general-purpose frameworks for model specification, Hamiltonian construction, and hardware-aware mappings. We advocate for a modular model-driven engineering (MDE) approach that supports different types of quantum simulation (digital and analogue), and facilitates automation, performance evaluation, and reusability. Through an example from high-energy physics, we outline a vision for a quantum simulation framework capable of supporting scalable, cross-platform simulation workflows.

quant-ph↗

arXiv2025

QEF: Reproducible and Exploratory Quantum Software Experiments

Commercially available Noisy Intermediate-Scale Quantum (NISQ) devices now make small hybrid quantum-classical experiments practical, but many tools hide configuration or demand ad-hoc scripting. We introduce the Quantum Experiment Framework (QEF): A lightweight framework designed to support the systematic, hypothesis-driven study of quantum algorithms. Unlike many existing approaches, QEF emphasises iterative, exploratory analysis of evolving experimental strategies rather than exhaustive empirical evaluation of fixed algorithms using predefined quality metrics. The framework's design is informed by a comprehensive review of the literature, identifying principal parameters and measurement practices currently reported in the field. QEF captures all key aspects of quantum software and algorithm experiments through a concise specification that expands into a Cartesian product of variants for controlled large-scale parameter sweeps. This design enables rigorous and systematic evaluation, as well as precise reproducibility. Large sweeps are automatically partitioned into asynchronous jobs across simulators or cloud hardware, and ascertain full hyper-parameter traceability. QEF supports parameter reuse to improve overall experiment runtimes, and collects all metrics and metadata into a form that can be conveniently explored with standard statistical and visualisation software. By combining reproducibility and scalability while avoiding the complexities of full workflow engines, QEF seeks to lower the practical barriers to empirical research on quantum algorithms, whether these are designed for current NISQ devices or future error-corrected quantum systems.

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arXiv2025

Out of the Loop: Structural Approximation of Optimisation Landscapes and non-Iterative Quantum Optimisation

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.

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arXiv2025

Hybrid Mixed Integer Linear Programming for Large-Scale Join Order Optimisation

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.

cs.DB↗

arXiv2025

Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models

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 Ansätze. 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.

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arXiv2025

From Tracepoints to Timeliness: A Semi-Markov Framework for Predictive Runtime Analysis

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.

cs.OS↗

arXiv2025

Towards System-Level Quantum-Accelerator Integration

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.

quant-ph↗

arXiv2025

Predict and Conquer: Navigating Algorithm Trade-offs with Quantum Design Automation

Combining quantum computers with classical compute power has become a standard means for developing algorithms that are eventually supposed to beat any purely classical alternatives. While in-principle advantages for solution quality or runtime are expected for many approaches, substantial challenges remain: Non-functional properties like runtime or solution quality of many approaches are not fully understood, and need to be explored empirically. This makes it unclear which approach is best suited for a given problem. Accurately predicting behaviour of quantum-classical algorithms opens possibilities for software abstraction layers, which can automate decision-making for algorithm selection and parametrisation. While such techniques find frequent use in classical high-performance computing, they are still mostly absent from quantum toolchains. We present a methodology to perform algorithm selection based on desirable non-functional requirements. This simplifies decision-making processes for users. Based on annotations at the source code level, our framework traces key characteristics of quantum-classical 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 quantum-classical 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 effort. We argue that our methodology generalises to problems beyond combinatorial optimisation, such as Hamiltonian simulation, and lays a foundation for integrated software layers for quantum design automation.

quant-ph↗

arXiv2025

CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cutting

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.

quant-ph↗

arXiv2025

QML Essentials -- A framework for working with Quantum Fourier Models

In this work, we propose a framework in the form of a Python package, specifically designed for the analysis of Quantum Machine Learning models. This framework is based on the PennyLane simulator and facilitates the evaluation and training of Variational Quantum Circuits. It provides additional functionality ranging from the ability to add different types of noise to the classical simulation, over different parameter initialisation strategies, to the calculation of expressibility and entanglement for a given model. As an intrinsic property of Quantum Fourier Models, it provides two methods for calculating the corresponding Fourier spectrum: one via the Fast Fourier Transform and another analytical method based on the expansion of the expectation value using trigonometric polynomials. It also provides a set of predefined approaches that allow a fast and straightforward implementation of Quantum Machine Learning models. With this framework, we extend the PennyLane simulator with a set of tools that allow researchers a more convenient start with Quantum Fourier Models and aim to unify the analysis of Variational Quantum Circuits.

quant-ph↗

arXiv2025

Make Some Noise! Measuring Noise Model Quality in Real-World Quantum Software

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 use-case 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.

quant-ph↗

arXiv2025

Stacking the Odds: Full-Stack Quantum System Design Space Exploration

Design space exploration (DSE) plays an important role in optimising quantum circuit execution by systematically evaluating different configurations of compilation strategies and hardware settings. In this work, we study the impact of layout methods, qubit routing techniques, compiler optimization levels, and hardware-specific properties, including noise characteristics, topological structures, connectivity densities, and device sizes. By traversing these dimensions, we aim to understand how compilation choices interact with hardware features. A central question in our study is whether carefully selected device parameters and mapping strategies, including initial layouts and routing heuristics, can mitigate hardware-induced errors beyond standard error mitigation methods. Our results show that choosing the right software strategies (e.g., layout and routing) and tailoring hardware properties (e.g., reducing noise or leveraging connectivity) significantly enhances the fidelity of quantum circuit executions. We provide performance estimates using metrics such as circuit depth, gate count, and expected fidelity. These findings highlight the value of hardware-software co-design, especially as quantum systems scale and move toward error-corrected computing. Our simulations, though noisy, include quantum error correction (QEC) scenarios, revealing similar sensitivities to layout and connectivity. This suggests that co-design principles will be vital for integrating QEC in future devices. Overall, we offer practical guidance for co-optimizing mapping, routing, and hardware configuration in real-world quantum computing.

quant-ph↗

arXiv2025

Guided-SPSA: Simultaneous Perturbation Stochastic Approximation assisted by the Parameter Shift Rule

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.

quant-ph↗

arXiv2025

SAT Strikes Back: Parameter and Path Relations in Quantum Toolchains

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.

quant-ph↗

arXiv2025

From Hope to Heuristic: Realistic Runtime Estimates for Quantum Optimisation in NHEP

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 optimisation 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, a key computing challenge in NHEP, and investigate how the classical parameter space in QAOA, along with techniques like a Fourier-analysis 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.

quant-ph↗

arXiv2025

Path Matters: Industrial Data Meet Quantum Optimization

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 real-world 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.

quant-ph↗

arXiv2025

Does the Tool Matter? Exploring Some Causes of Threats to Validity in Mining Software Repositories

Software repositories are an essential source of information for software engineering research on topics such as project evolution and developer collaboration. Appropriate mining tools and analysis pipelines are therefore an indispensable precondition for many research activities. Ideally, valid results should not depend on technical details of data collection and processing. It is, however, widely acknowledged that mining pipelines are complex, with a multitude of implementation decisions made by tool authors based on their interests and assumptions. This raises the questions if (and to what extent) tools agree on their results and are interchangeable. In this study, we use two tools to extract and analyse ten large software projects, quantitatively and qualitatively comparing results and derived data to better understand this concern. We analyse discrepancies from a technical point of view, and adjust code and parametrisation to minimise replication differences. Our results indicate that despite similar trends, even simple metrics such as the numbers of commits and developers may differ by up to 500%. We find that such substantial differences are often caused by minor technical details. We show how tool-level and data post-processing changes can overcome these issues, but find they may require considerable efforts. We summarise identified causes in our lessons learned to help researchers and practitioners avoid common pitfalls, and reflect on implementation decisions and their influence in ensuring obtained data meets explicit and implicit expectations. Our findings lead us to hypothesise that similar uncertainties exist in other analysis tools, which may limit the validity of conclusions drawn in tool-centric research.

cs.SE↗

arXiv2024

QCE'24 Tutorial: Quantum Annealing -- Emerging Exploration for Database Optimization

Quantum annealing is a meta-heuristic approach tailored to solve combinatorial optimization problems with quantum annealers. In this tutorial, we provide a fundamental and comprehensive introduction to quantum annealing and modern data management systems and show quantum annealing's potential benefits and applications in the realm of database optimization. We demonstrate how to apply quantum annealing for selected database optimization problems, which are critical challenges in many data management platforms. The demonstrations include solving join order optimization problems in relational databases, optimizing sophisticated transaction scheduling, and allocating virtual machines within cloud-based architectures with respect to sustainability metrics. On the one hand, the demonstrations show how to apply quantum annealing on key problems of database management systems (join order selection, transaction scheduling), and on the other hand, they show how quantum annealing can be integrated as a part of larger and dynamic optimization pipelines (virtual machine allocation). The goal of our tutorial is to provide a centralized and condensed source regarding theories and applications of quantum annealing technology for database researchers, practitioners, and everyone who wants to understand how to potentially optimize data management with quantum computing in practice. Besides, we identify the advantages, limitations, and potentials of quantum computing for future database and data management research.

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