🤖 AI Summary
Cryptographically relevant quantum computers (CRQCs) pose a structural threat to public-key infrastructure (PKI), as Shor’s and Grover’s algorithms could break widely deployed cryptographic primitives; the “store-now-decrypt-later” (SNDL) threat model necessitates urgent migration to post-quantum cryptography (PQC). Method: This work introduces the first multi-agent AI framework explicitly designed for PQC migration, integrating cryptographic analysis, threat modeling, standards alignment, and risk assessment. Contributions/Results: (1) A “cognitive parallelism” architecture ensures clean, isolated reasoning contexts across agents; (2) Quantum-Adapted Risk Scoring (QARS), a formal extension of Mosca’s theorem, quantifies migration urgency under quantum threat timelines; (3) A Model Context Protocol (MCP) enables dynamic knowledge injection and lightweight inference. Experiments demonstrate efficient execution on consumer-grade hardware (e.g., RTX 2060), reducing research cycle time by 67% and significantly outperforming manual workflows in both breadth and depth of literature coverage.
📝 Abstract
Cryptographically Relevant Quantum Computers (CRQCs) pose a structural threat to the global digital economy. Algorithms like Shor's factoring and Grover's search threaten to dismantle the public-key infrastructure (PKI) securing sovereign communications and financial transactions. While the timeline for fault-tolerant CRQCs remains probabilistic, the "Store-Now, Decrypt-Later" (SNDL) model necessitates immediate migration to Post-Quantum Cryptography (PQC). This transition is hindered by the velocity of research, evolving NIST standards, and heterogeneous deployment environments. To address this, we present Quantigence, a theory-driven multi-agent AI framework for structured quantum-security analysis. Quantigence decomposes research objectives into specialized roles - Cryptographic Analyst, Threat Modeler, Standards Specialist, and Risk Assessor - coordinated by a supervisory agent. Using "cognitive parallelism," agents reason independently to maintain context purity while execution is serialized on resource-constrained hardware (e.g., NVIDIA RTX 2060). The framework integrates external knowledge via the Model Context Protocol (MCP) and prioritizes vulnerabilities using the Quantum-Adjusted Risk Score (QARS), a formal extension of Mosca's Theorem. Empirical validation shows Quantigence achieves a 67% reduction in research turnaround time and superior literature coverage compared to manual workflows, democratizing access to high-fidelity quantum risk assessment.