2025/08/22 by Schuh, Günther, Bennemann, Frederik, Gagel, Rainer
#600 | Technik #Artificial Intelligence #Generic application types #Optimization #Quantum computing #Simulation #TRIZ #Use case identification
paper · doi:10.15488/19418
Quantum computing (QC) holds transformative potential across industries, promising exponential performance improvements in specific applications. However, identifying future QC use cases remains challenging due to its relative immaturity and inherent complexity. Building upon our previous work where we introduced a TRIZ-inspired framework for systematically identifying future applications, this paper contextualizes the framework for QC by defining generic solution types. We conduct a systematic literature review, identifying 46 QC use cases across four archetypes: optimization, simulation, artificial intelligence (AI) , and cryptography. By examining the constituent elements of each use case task, the application object and the goal, we develop a comprehensive framework for their analysis. Our findings reveal that application objects can be classified by nature (material, technical, social) and scale (elementary object, system, network), leading to the identification of 15 generic application types for QC. For example, in technical networks, QC can optimize resource allocation or predict the dynamic behavior of system components, while in material systems, it can accelerate the development of new materials. Artificial intelligence applications are identified as cross-sectional use cases applicable across various domains. By formulating these generic application types, our framework offers a structured approach for organizations to identify potential QC applications relevant to their specific contexts, following the logic of TRIZ.