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Researcher profile

Frank Leymann

Frank Leymann contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
quant-phSoftware EngineeringArtificial IntelligenceDistributed, Parallel, and Cluster Computingmath.HOmath.NT

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Published work

6 published item(s)

preprint2026arXiv

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 $τ$-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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Continued Fractions and Probability Estimations in the Shor Algorithm -- A Detailed and Self-Contained Treatise

The algorithm of Shor for prime factorization is a hybrid algorithm consisting of a quantum part and a classical part. The main focus of the classical part is a continued fraction analysis. The presentation of this is often short, pointing to text books on number theory. In this contribution, we present the relevant results and proofs from the theory of continued fractions in detail (even in more detail than in text books) filling the gap to allow a complete comprehension of the algorithm of Shor. Similarly, we provide a detailed computation of the estimation of the probability that convergents will provide the period required for determining a prime factor.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Hybrid Quantum Applications Need Two Orchestrations in Superposition: A Software Architecture Perspective

Quantum applications are most often hybrid, i.e. they are not only made of implementations of pure quantum algorithms but also of classical programs as well as workflows and topologies as key artifacts, and data they process. Since workflows and topologies are referred to as orchestrations in modern terminology (but with very different meaning), two orchestrations that go hand-in-hand are required to realize quantum applications. We motivate this by means of a non-trivial example, sketch these orchestration technologies and reveal the overall structure of nontrivial quantum applications as well as the implied architecture of a runtime environment for such applications.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

FaaSten Your Decisions: Classification Framework and Technology Review of Function-as-a-Service Platforms

Function-as-a-Service (FaaS) is a cloud service model enabling developers to offload event-driven executable snippets of code. The execution and management of such functions becomes a FaaS provider's responsibility, hereby included their on-demand provisioning and automatic scaling. Key enablers for this cloud service model are FaaS platforms, e.g., AWS Lambda, Microsoft Azure Functions or OpenFaaS. At the same time, the choice of the most appropriate FaaS platform for deploying and running a serverless application is not trivial, as various organizational and technical aspects have to be taken into account. In this work, we present (i) a FaaS platform classification framework derived using a mixed method study and (ii) a systematic technology review of the ten most prominent FaaS platforms, based on the proposed classification framework. Moreover, we present (iii) a FaaS platform selection support system, called \faastener, which helps researchers and practitioners to choose the FaaS platform most suited for their requirements.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Pattern Views: Concept and Tooling for Interconnected Pattern Languages

Patterns describe proven solutions for recurring problems. Typically, patterns in a particular domain are interrelated and organized in pattern languages. As real-world problems often require patterns of multiple domains, different pattern languages have to be considered to address these problems. However, cross-domain knowledge about how patterns of different languages relate to each other is either hidden in individual pattern descriptions or not documented at all. This makes it difficult to identify relevant patterns across pattern languages. Therefore, we introduce a concept and tooling that enables to capture patterns and their relations across pattern languages for a particular problem context.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Quantum in the Cloud: Application Potentials and Research Opportunities

Quantum computers are becoming real, and they have the inherent potential to significantly impact many application domains. We sketch the basics about programming quantum computers, showing that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts. With the advent of quantum computers in the cloud, the cloud is a fine environment for performing quantum programs. The tool chain available for creating and running such programs is sketched. As an exemplary problem we discuss efforts to implement quantum programs that are hardware independent. A use case from machine learning is outlined. Finally, a collaborative platform for solving problems with quantum computers that is currently under construction is presented.

0 citations0 reviewsNo code linkedOpen access