Paper Number
ICIS2025-1712
Paper Type
Short
Abstract
Technological evolution is often portrayed as linear and path-dependent, overlooking the phenomenon of technological back-pedaling, where older technologies resurface to enable advancements in novel ones. We explore this interplay, focusing on Quantum Computing (QC) and Artificial Intelligence (AI). While QC holds potential for many fields, its progress is increasingly intertwined with AI, which aids in algorithm design, error correction, and circuit optimization. Such reliance, however, introduces challenges, including systemic biases and resource constraints, which may limit the development of QC. We employ phenomenon-driven theorizing and action research to examine how mainstream technologies can both enable and constrain emergent ones. We elucidate the dynamics of technological interdependence, aiming to contribute to the literature on information systems implementation and adoption by identifying strategies for optimizing synergies and mitigating bottlenecks. Such an exploration of a timely and underexplored topic can provide a critical lens for understanding nuanced affordances of technological evolution.
Recommended Citation
De Putron, Natalia and Angelopoulos, Spyros, "Technological Back-Pedaling: Bridging Quantum Computing and Artificial Intelligence through Phenomenon-Driven Theorizing and Action Research" (2025). ICIS 2025 Proceedings. 3.
https://aisel.aisnet.org/icis2025/quantum/quantum/3
Technological Back-Pedaling: Bridging Quantum Computing and Artificial Intelligence through Phenomenon-Driven Theorizing and Action Research
Technological evolution is often portrayed as linear and path-dependent, overlooking the phenomenon of technological back-pedaling, where older technologies resurface to enable advancements in novel ones. We explore this interplay, focusing on Quantum Computing (QC) and Artificial Intelligence (AI). While QC holds potential for many fields, its progress is increasingly intertwined with AI, which aids in algorithm design, error correction, and circuit optimization. Such reliance, however, introduces challenges, including systemic biases and resource constraints, which may limit the development of QC. We employ phenomenon-driven theorizing and action research to examine how mainstream technologies can both enable and constrain emergent ones. We elucidate the dynamics of technological interdependence, aiming to contribute to the literature on information systems implementation and adoption by identifying strategies for optimizing synergies and mitigating bottlenecks. Such an exploration of a timely and underexplored topic can provide a critical lens for understanding nuanced affordances of technological evolution.
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11-Quantum