CAQSG — Correlated AI/Quantum Spectral Graphing

CAQSG treats noisy multi-source signals as a graph and applies spectral techniques borrowed from quantum information theory to surface correlated structure that single-channel models miss.

Originally developed in the ESG / GenCarbon context, CAQSG also underpins RiyaRisk weak-signal detection and the correlation layer inside StressTrace.

Published as an SSRN preprint (abstract 6618401).

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Published by Jumpstart AI and Quantum Labs. Co-founders: Dr. Nupur Mukherjee (Chief Science Officer) and Kishan Sathyan (Chief GTM Officer). Research cited as SSRN preprints; JAQL technology is quantum-inspired and runs on classical infrastructure.