Quantigence: A Multi-Agent AI Framework for Quantum Security Research
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915675402928128 |
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| author | Alquwayfili, Abdulmalik |
| author_facet | Alquwayfili, Abdulmalik |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_12989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantigence: A Multi-Agent AI Framework for Quantum Security Research Alquwayfili, Abdulmalik Multiagent Systems Cryptography and Security 94A60, 68T42 K.6.5; I.2.11; E.3 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. |
| title | Quantigence: A Multi-Agent AI Framework for Quantum Security Research |
| topic | Multiagent Systems Cryptography and Security 94A60, 68T42 K.6.5; I.2.11; E.3 |
| url | https://arxiv.org/abs/2512.12989 |