A Study on the Application of Future Signal Prediction Techniques for Policy Development in Quantum Information Science

Quantum information science has recently emerged as a key technological domain influencing national strategies and industrial competitiveness, particularly in advanced fields such as quantum computing, quantum sensing, and quantum communication.Despite increasing recognition of its importance, systematic analysis of public discourse to identify future-oriented signals in quantum information science remains insufficient.To address this gap, this study employs big data analytics, specifically text mining, visual analytics, and quantitative methods, to explore future signals within public discussions on quantum information science.In Particular, two core analytic models-Keyword Issue Map (KIM) and Keyword Emergence Map (KEM)-were employed to track issue salience and detect emerging terms, enabling classification of signals into strong, weak, latent, and well-known types.Data was collected from various public sources, and analytical techniques such as keyword frequency analysis, morphological contextual analysis, and network mapping were applied to identify and categorize signals according to their strength and potential impact.Results revealed distinct signal categories: strong signals (e.g., nation, security, investment) closely connected to current government policy and industrial priorities, and weaker signals (e.g., regulation, control, biotechnology, convergence), which, while currently peripheral, show substantial potential for future emergence.Furthermore, distinct public reactions corresponding directly to governmental announcements and strategic investments were identified, demonstrating clear linkages between policy actions and public perception.These findings suggest that quantum information science discourse is characterized by mature and nascent issues, underscoring the necessity of proactive policy roadmaps, targeted R&D investments, and enhanced industry-academic collaborations.