About Quantum AI

Plain definitions of the concepts JAQL works in, with links to the relevant research, explainers and products.

Quantum computing

A computing model that uses quantum mechanical effects (superposition, entanglement) to solve specific problems faster than classical computers. Fault-tolerant general-purpose quantum computers do not yet exist at deployment scale. See Quantum AI vs classical AI.

Quantum-inspired AI

Classical implementations of techniques borrowed from quantum information theory — spectral methods, tensor-network compression, amplitude-style routing — that improve cost, signal-extraction and explainability versus a pure-LLM stack. This is what JAQL ships. See Research.

Quantum cybersecurity

Defending data and decisions against threats that exploit or anticipate quantum-class capability. See What is quantum cybersecurity? and StressTrace.

Post-quantum cryptography

Cryptography (FIPS 203 ML-KEM, FIPS 204 ML-DSA, FIPS 205 SLH-DSA) designed to resist attack by quantum computers. See Post-quantum security explained and Post-Quantum Security.

Quantum SOC

A security operations centre that adds quantum-inspired detection and post-quantum-safe transmission to a classical SOC stack. See What is a quantum SOC?.

AI governance

The discipline of governing AI decisions in regulated industries — bound policy, per-decision explainability, drift monitoring, audit trails and human escalation. See AI governance for banks and the five principles.

Quantum sensing

Use of quantum effects (e.g. nitrogen-vacancy diamond magnetometry) to detect signals classical sensors cannot. See Quantum sensing for rescue and detection and JAQLNV-7.

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.