Warm-Starting and Quantum Computing: A Systematic Mapping Study
Felix Truger, Johanna Barzen, Marvin Bechtold, Martin Beisel, Frank Leymann, Alexander Mandl, Vladimir Yussupov
TL;DR
The paper tackles the challenge of limited qubit counts and error-prone quantum hardware by surveying warm-starting techniques in quantum computing. It uses a systematic mapping study to compile a dataset of $80$ publications across six databases and snowballing, deriving a property-based classification that reveals five major technique classes and ten subclasses. The study finds a growing, primarily academic-driven body of work focused on variational quantum algorithms (notably $QAOA$, $QNN$, and $VQE$) with goals centered on speedups, accuracy, and resource reduction, while providing a practical taxonomy to guide researchers and practitioners. This work advances understanding of warm-starting in the quantum domain and lays groundwork for future research on technique compatibility, practical deployment, and decision-support tools in quantum software engineering.
Abstract
Due to low numbers of qubits and their error-proneness, Noisy Intermediate-Scale Quantum (NISQ) computers impose constraints on the size of quantum algorithms they can successfully execute. State-of-the-art research introduces various techniques addressing these limitations by utilizing known or inexpensively generated approximations, solutions, or models as a starting point to approach a task instead of starting from scratch. These so-called warm-starting techniques aim to reduce quantum resource consumption, thus facilitating the design of algorithms suiting the capabilities of NISQ computers. In this work, we collect and analyze scientific literature on warm-starting techniques in the quantum computing domain. In particular, we (i) create a systematic map of state-of-the-art research on warm-starting techniques using established guidelines for systematic mapping studies, (ii) identify relevant properties of such techniques, and (iii) based on these properties classify the techniques identified in the literature in an extensible classification scheme. Our results provide insights into the research field and aim to help quantum software engineers to categorize warm-starting techniques and apply them in practice. Moreover, our contributions may serve as a starting point for further research on the warm-starting topic since they provide an overview of existing work and facilitate the identification of research gaps.