The Convergence of Quantum Computing and Artificial Intelligence as Emerging Threats to National Cybersecurity: A Mixed-Methods Analysis of Compound Threat Vectors
English | June 2, 2026 | ASIN: B0H3MQ6XQK | 518 pages | Epub | 2.02 MB
The concurrent maturation of quantum computing and artificial intelligence (AI) presents a compound threat to national cybersecurity that transcends the risks posed by either technology in isolation. Despite growing recognition of quantum and AI as individual security challenges, no comprehensive empirical investigation has examined how their convergence produces synergistic threat vectors that existing governance frameworks are structurally unprepared to address. This mixed-methods sequential explanatory study investigated the nature, trajectory, and governance of converged quantum-AI threats through three phases. Phase 1 conducted quantitative analysis of secondary data from the MITRE ATT&CK/ATLAS framework, the National Vulnerability Database (NVD), the AI Incident Database (AIID), and quantum computing hardware benchmarks (2018-2025, n = 8 annual observations) using Spearman rank-order correlations, autoregressive integrated moving average (ARIMA) time-series modeling, rolling-origin cross-validation, and a weighted Composite Threat Index. Phase 2 employed qualitative thematic analysis of 32 national security policy documents using a hybrid deductive-inductive coding approach informed by Braun and Clarke's framework. Phase 3 integrated both strands through a joint display methodology to develop the Quantum-AI Threat Convergence Framework (QATCF). Quantitative findings revealed synchronized acceleration across all threat indicators: very strong co-trending between AI technique proliferation and incident growth (ρ = .976, p <.001), between quantum hardware advancement and AI vulnerability disclosure (ρ = .952, p <.001), and a 17-fold increase in the Composite Threat Index (4.5 to 78.2) over the study period. A sanity-check audit established that most correlations reflected shared temporal trends rather than direct causation, recalibrating interpretive claims as evidence of co-trending convergence. ARIMA projections indicated cybersecurity-specific AI incidents may nearly double to 206 by 2028. Qualitative analysis identified seven themes, with the most consequential being a "Convergence Blind Spot"-a persistent structural gap in which policy instruments address quantum and AI threats through separate institutional channels, leaving four of ten compound threat categories entirely unaddressed. The QATCF organized the compound threat landscape into four categories, three of which remain completely unaddressed by current governance. These findings demonstrate that the convergent quantum-AI threat is real, measurably accelerating, and inadequately governed, necessitating an integrated defense paradigm that transcends the siloed approaches characterizing current national cybersecurity policy.
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