arXiv subjects

Frank Leymann

Publications and source records attributed to Frank Leymann.

At least 19 recordsLinked to original sources

arXiv2026

A Methodology for Investigating AI Patterns Prevalence in Software Repositories

As Artificial Intelligence(AI)-based applications take off, a clear understanding of AI patterns can uplift the quality of AI applications. Many AI patterns have been proposed in the literature; however, their prevalence in real-life code has not yet been validated. Understanding the actual use of those patterns in practice can clarify our understanding both of the significance of these patterns and their utility. In this paper, we present a methodology to a) identify relevant patterns by mining the literature and then to b) validate their presence and prevalence in actual code repositories using active learning. To that end, we identify 14 AI pattern classes by mining 44 published AI pattern-related sources. Then we use an active learning approach to determine the prevalence of the most common pattern class across 100 GitHub open AI repositories. Using prevalence estimation, we propose bounds on the accuracy of the occurrences. The model achieves 56\% accuracy and 55\% recall in an 8-way classification task, significantly outperforming the 11\% random-chance baseline. Furthermore, the prevalence estimation offers usable bounds for analyzing pattern applications. This methodology provides a robust foundation to start understanding how AI patterns are used in practice, a field that currently lacks empirical data.

cs.SE↗

arXiv2026

Mathematics of Spacetime: A Guided Tour Through The Underlying Differential Topology and Differential Geometry

Background in General Relativity (e.g. black holes, wormholes, or spacetime models in general) is needed to comprehend more recent efforts around understanding quantum phenomena like entanglement (e.g. >>It from qubit<< as well as >>ER = EPR<<). The former in turn requires a lot of knowledge from differential topology and differential geometry. While this knowledge is available in very good mathematics textbooks, it is scattered i.e. quite a bunch of sources need to be consulted to acquire it. The goal of this contribution is to provide the major background in a single place; in this sense, this contribution is some sort of guided tour through the corresponding literature.

physics.gen-ph↗

arXiv2026

Robust Agent Compensation (RAC): Teaching AI Agents to Compensate

We present Robust Agent Compensation (RAC), a log-based recovery paradigm (providing a safety net) implemented through an architectural extension that can be applied to most Agent frameworks to support reliable executions (avoiding unintended side effects). Users can choose to enable RAC without changing their current agent code (e.g., LangGraph agents). The proposed approach can be implemented in most existing agent frameworks via their existing extension points. We present an implementation based on LangChain, demonstrate its viability through the $\tau$-bench and REALM-Bench, and show that when solving complex problems, RAC is 1.5-8X or more better in both latency and token economy compared to state-of-the-art LLM-based recovery approaches.

cs.AI↗

arXiv2025

Loss Behavior in Supervised Learning with Entangled States

Quantum Machine Learning (QML) aims to leverage the principles of quantum mechanics to speed up the process of solving machine learning problems or improve the quality of solutions. Among these principles, entanglement with an auxiliary system was shown to increase the quality of QML models in applications such as supervised learning. Recent works focus on the information that can be extracted from entangled training samples and their effect on the approximation error of the trained model. However, results on the trainability of QML models show that the training process itself is affected by various properties of the supervised learning task. These properties include the circuit structure of the QML model, the used cost function, and noise on the quantum computer. To evaluate the applicability of entanglement in supervised learning, we augment these results by investigating the effect of highly entangled training data on the model's trainability. In this work, we show that for highly expressive models, i.e., models capable of expressing a large number of candidate solutions, the possible improvement of loss function values in constrained neighborhoods during optimization is severely limited when maximally entangled states are employed for training. Furthermore, we support this finding experimentally by simulating training with Parameterized Quantum Circuits (PQCs). Our findings show that as the expressivity of the PQC increases, it becomes more susceptible to loss concentration induced by entangled training data. Lastly, our experiments evaluate the efficacy of non-maximal entanglement in the training samples and highlight the fundamental role of entanglement entropy as a predictor for the trainability.

quant-ph↗

arXiv2025

On the Differential Topology of Expressivity of Parameterized Quantum Circuits

Parameterized quantum circuits play a key role in quantum computing. Measuring the suitability of such a circuit for solving a class of problems is needed. One such promising measure is the expressivity of a circuit, which is defined in two main variants. The variant in focus of this contribution is the so-called dimensional expressivity which measures the dimension of the submanifold of states produced by the circuit. Understanding this measure needs a lot of background from differential topology which makes it hard to comprehend. In this article we provide this background in a vivid as well as pedagogical manner. Especially it strives towards being self-contained for understanding expressivity, e.g. the required mathematical foundations are provided and examples are given. Also, the literature makes several statements about expressivity the proofs of which are omitted or only indicated. In this article we give proofs for key statements from dimensional expressivity, sometimes revealing limits for generalizing them, and also sketching how to proceed in practice to determine this measure.

quant-ph↗

arXiv2025

Simulating Quantum State Transfer between Distributed Devices using Noisy Interconnects

Scaling beyond individual quantum devices via distributed quantum computing relies critically on high-fidelity quantum state transfers between devices, yet the quantum interconnects needed for this are currently unavailable or expected to be significantly noisy. These limitations can be bypassed by simulating ideal state transfer using quasiprobability decompositions (QPDs). Wire cutting, for instance, allows this even without quantum interconnects. Nevertheless, QPD methods face drawbacks, requiring sampling from multiple circuit variants and incurring substantial sampling overhead. While prior theoretical work showed that incorporating noisy interconnects within QPD protocols could reduce sampling overhead relative to interconnect quality, a practical implementation for realistic conditions was lacking. Addressing this gap, this work presents a generalized and practical QPD for state transfer simulation using noisy interconnects to reduce sampling overhead. The QPD incorporates a single tunable parameter for straightforward calibration to any utilized interconnect. To lower practical costs, the work also explores reducing the number of distinct circuit variants required by the QPD. Experimental validation on contemporary quantum devices confirms the proposed QPD's practical feasibility and expected sampling overhead reduction under realistic noise. Notably, the results show higher effective state transfer fidelity than direct transfer over the underlying noisy interconnect.

quant-ph↗

arXiv2025

Harnessing Patterns to Support the Development of Hybrid Quantum Applications

Quantum computing provides computational advantages in various domains. To benefit from these advantages complex hybrid quantum applications must be built, which comprise both quantum and classical programs. Engineering these applications requires immense expertise in physics, mathematics, and software engineering. To facilitate the development of quantum applications, a corresponding quantum computing pattern language providing proven solutions to recurring problems has been presented. However, identifying suitable patterns for tackling a specific application scenario and subsequently combining them in an application is a time-consuming manual task. To overcome this issue, we present an approach that enables (i) the automated detection of patterns solving a given problem, (ii) the selection of suitable implementations fulfilling non-functional requirements of the user, and (iii) the automated aggregation of these solutions into an executable quantum application.

quant-ph↗

arXiv2024

Qunicorn: A Middleware for the Unified Execution Across Heterogeneous Quantum Cloud Offerings

Quantum computers are available via a variety of different quantum cloud offerings. These offerings are heterogeneous and differ in features, such as pricing models or types of access to quantum computers. Furthermore, quantum circuits can be implemented using different quantum programming languages, which are typically only supported by a small subset of quantum cloud offerings. As a consequence, using a specific quantum programming language for implementing the application at hand can limit the set of compatible quantum cloud offerings and cause a vendor lock-in. Therefore, selecting a suitable quantum cloud offering and a corresponding quantum programming language requires knowledge about their features. In this paper, we (i) analyze the available quantum cloud offerings and extract their features. Moreover, we (ii) introduce the architecture for a unification middleware that facilitates accessing quantum computers available via different quantum cloud offerings by automatically translating between various quantum circuit and result formats. To showcase the practical feasibility of our approach, we (iii) present a prototypical implementation and validate it for three exemplary application scenarios.

quant-ph↗

arXiv2024

Joint Wire Cutting with Non-Maximally Entangled States

Distributed quantum computing leverages the collective power of multiple quantum devices to perform computations exceeding the capabilities of individual quantum devices. A currently studied technique to enable this distributed approach is wire cutting, which decomposes a quantum circuit into smaller subcircuits by cutting their connecting wires. These subcircuits can then be executed on distributed devices, and their results are classically combined to reconstruct the original computation's result. However, wire cutting requires additional circuit executions to preserve result accuracy, with their number growing exponentially with each cut. Thus, minimizing this sampling overhead is crucial for reducing the total execution time. Employing shared non-maximally entangled (NME) states between distributed devices reduces this overhead for single wire cuts, moving closer to ideal teleportation with maximally entangled states. Extending this approach to jointly cutting multiple wires using NME states remained unexplored. Our paper addresses this gap by investigating the use of NME states for joint wire cuts, aiming to reduce the sampling overhead further. Our three main contributions include (i) determining the minimal sampling overhead for this scenario, (ii) analyzing the overhead when using composite NME states constructed from smaller NME states, and (iii) introducing a wire cutting technique that achieves the optimal sampling overhead with pure NME states, paving the way towards wire cutting with arbitrary NME states.

quant-ph↗

arXiv2024

Post-Quantum Security: Origin, Fundamentals, and Adoption

Nowadays, predominant asymmetric cryptographic schemes are considered to be secure because discrete logarithms are believed to be hard to be computed. The algorithm of Shor can effectively compute discrete logarithms, i.e. it can brake such asymmetric schemes. But the algorithm of Shor is a quantum algorithm and at the time this algorithm has been invented, quantum computers that may successfully execute this algorithm seemed to be far out in the future. The latter has changed: quantum computers that are powerful enough are likely to be available in a couple of years. In this article, we first describe the relation between discrete logarithms and two well-known asymmetric security schemes, RSA and Elliptic Curve Cryptography. Next, we present the foundations of lattice-based cryptography which is the bases of schemes that are considered to be safe against attacks by quantum algorithms (as well as by classical algorithms). Then we describe two such quantum-safe algorithms (Kyber and Dilithium) in more detail. Finally, we give a very brief and selective overview of a few actions currently taken by governments and industry as well as standardization in this area. The article especially strives towards being self-contained: the required mathematical foundations to understand post-quantum cryptography are provided and examples are given.

cs.CR↗

arXiv2024

Quantum Software Engineering: Roadmap and Challenges Ahead

As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective -key qualities of any industry-grade software-mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyse in depth the current state of quantum software engineering research. From this analysis, the key areas of quantum software engineering are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing Quantum Software Engineering.

cs.SE↗

arXiv2024

Warm-Starting the VQE with Approximate Complex Amplitude Encoding

The Variational Quantum Eigensolver (VQE) is a Variational Quantum Algorithm (VQA) to determine the ground state of quantum-mechanical systems. As a VQA, it makes use of a classical computer to optimize parameter values for its quantum circuit. However, each iteration of the VQE requires a multitude of measurements, and the optimization is subject to obstructions, such as barren plateaus, local minima, and subsequently slow convergence. We propose a warm-starting technique, that utilizes an approximation to generate beneficial initial parameter values for the VQE aiming to mitigate these effects. The warm-start is based on Approximate Complex Amplitude Encoding, a VQA using fidelity estimations from classical shadows to encode complex amplitude vectors into quantum states. Such warm-starts open the path to fruitful combinations of classical approximation algorithms and quantum algorithms. In particular, the evaluation of our approach shows that the warm-started VQE reaches higher quality solutions earlier than the original VQE.

quant-ph↗

arXiv2024

Cutting a Wire with Non-Maximally Entangled States

Distributed quantum computing supports combining the computational power of multiple quantum devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum computations via classical communication. These techniques involve partitioning a quantum circuit into smaller subcircuits, each containing fewer qubits. The original circuit's outcome can be replicated by executing these subcircuits on separate devices and combining their results. However, the number of circuit executions required to achieve a fixed result accuracy with circuit cutting grows exponentially with the number of cuts, posing significant costs. In contrast, quantum teleportation allows the distribution of quantum computations without an exponential increase in circuit executions. Nevertheless, each teleportation requires a pre-shared pair of maximally entangled qubits for transmitting a quantum state, and non-maximally entangled qubits cannot be used for this purpose. Addressing this, our work explores utilizing non-maximally entangled qubit pairs in wire cutting, a specific form of circuit cutting, to mitigate the associated costs. The cost of this cutting procedure reduces with the increasing degree of entanglement in the pre-shared qubit pairs. We derive the optimal sampling overhead in this context and present a wire cutting technique employing pure non-maximally entangled states that achieves this optimal sampling overhead. Hence, this offers a continuum between existing wire cutting and quantum teleportation.

quant-ph↗

arXiv2023

Comparing Quantum Service Offerings

With the emergence of quantum computing, a growing number of quantum devices is accessible via cloud offerings. However, due to the rapid development of the field, these quantum-specific service offerings vary significantly in capabilities and requirements they impose on software developers. This is particularly challenging for practitioners from outside the quantum computing domain who are interested in using these offerings as parts of their applications. In this paper, we compare several devices based on different hardware technologies and provided through different offerings, by conducting the same experiment on each of them. By documenting the lessons learned from our experiments, we aim to simplify the usage of quantum-specific offerings and illustrate the differences between predominant quantum hardware technologies.

quant-ph↗

arXiv2023

On Reducing the Amount of Samples Required for Training of QNNs: Constraints on the Linear Structure of the Training Data

Training classical neural networks generally requires a large number of training samples. Using entangled training samples, Quantum Neural Networks (QNNs) have the potential to significantly reduce the amount of training samples required in the training process. However, to minimize the number of incorrect predictions made by the resulting QNN, it is essential that the structure of the training samples meets certain requirements. On the one hand, the exact degree of entanglement must be fixed for the whole set of training samples. On the other hand, training samples must be linearly independent and non-orthogonal. However, how failing to meet these requirements affects the resulting QNN is not fully studied. To address this, we extend the proof of the QNFL theorem to (i) provide a generalization of the theorem for varying degrees of entanglement. This generalization shows that the average degree of entanglement in the set of training samples can be used to predict the expected quality of the QNN. Furthermore, we (ii) introduce new estimates for the expected accuracy of QNNs for moderately entangled training samples that are linear dependent or orthogonal. Our analytical results are (iii) experimentally validated by simulating QNN training and analyzing the quality of the QNN after training.

quant-ph↗

arXiv2023

Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices

Noisy Intermediate-Scale Quantum (NISQ) devices are restricted by their limited number of qubits and their short decoherence times. An approach addressing these problems is quantum circuit cutting. It decomposes the execution of a large quantum circuit into the execution of multiple smaller quantum circuits with additional classical postprocessing. Since these smaller quantum circuits require fewer qubits and gates, they are more suitable for NISQ devices. To investigate the effect of quantum circuit cutting in a quantum algorithm targeting NISQ devices, we design two experiments using the Quantum Approximate Optimization Algorithm (QAOA) for the Maximum Cut (MaxCut) problem and conduct them on state-of-the-art superconducting devices. Our first experiment studies the influence of circuit cutting on the objective function of QAOA, and the second evaluates the quality of results obtained by the whole algorithm with circuit cutting. The results show that circuit cutting can reduce the effects of noise in QAOA, and therefore, the algorithm yields better solutions on NISQ devices.

quant-ph↗

arXiv2023

Circuit Cutting with Non-Maximally Entangled States

Distributed quantum computing combines the computational power of multiple devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum computations through classical communication. These techniques involve partitioning a quantum circuit into smaller subcircuits, each containing fewer qubits. The original circuit's outcome can be replicated by executing these subcircuits on separate devices and combining their results. However, the number of shots required to achieve a fixed result accuracy with circuit cutting grows exponentially with the number of cuts, posing significant costs. In contrast, quantum teleportation allows the distribution of quantum computations without an exponential increase in shots. Nevertheless, each teleportation procedure requires a pre-shared pair of maximally entangled qubits for transmitting a quantum state, and non-maximally entangled qubits cannot be used for this purpose. To address this, we propose a novel circuit cutting technique that leverages non-maximally entangled qubit pairs, effectively reducing the cost associated with circuit cutting. By considering the degree of entanglement in the pre-shared qubit pairs, our technique provides a continuum between existing circuit cutting methods and quantum teleportation, adjusting the cost of circuit cutting accordingly.

quant-ph↗

arXiv2023

Architectural Vision for Quantum Computing in the Edge-Cloud Continuum

Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, this work presents an architecture for Quantum Computing in the edge-cloud continuum. We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum. Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity. We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum. To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality. Specifically, we implement a split neural network with optional hybrid QNN predictors. Our results show that extending classical methods with QNNs is viable and promising for future work.

quant-ph↗