Quantum computing and risk prediction accuracy: an analysis of IT companies' risk appetite

This study analyses how quantum computing QAE improves IT risk appetite prediction. The study contains quantitative surveys of 256 IT specialists and qualitative interviews with ten industry professionals. The paper explores how QAE influences market volatility, infrastructure compatibility, data privacy and security, historical data availability, and risk appetite forecast accuracy. SEM and CFA testing show construct validity and model fitness, showing habit theory can predict outcomes (CFI = 0.97, RMSEA = 0.05, SRMR = 0.03). The R-square value for this regression study is slightly above 72.7%, with accuracy, accessibility, and data privacy/security being the primary factors influencing risk appetite forecasts (β = 0.235, p < 0.001). All components are positively correlated at 0.7210.765, while QAE moderates risk appetite by 0.38. Discriminant validity evaluates construct differences and assures reasonable links. The study found that quantum computing can alter IT risk management and uncover application and data handling issues. This study shows how to create quantum-enhanced risk models and assess IT industry quantum computing preparedness. Cross-sectional data, simulation-based analysis, future research, a longitudinal study, and quantum-resistant encryption risk management are briefly explored as study flaws. We improve quantum finance literature and help IT firms manage quantum risk.