Define a cohesive method to design quantum AI systems that are scalable, interpretable, and secure—treating quantum effects (coherence, entanglement, measurement) as architectural primitives tightly linked to learning, governance, and verification.
CRQC–LLM (adversarial stress-test lens) examines how quantum capability and automated AI reconnaissance may pressure-test trust. QALIS (constructive counter-architecture) embeds resilience so sensing, learning, and inference remain verifiable under stress.
The frameworks operate within QAIS (Quantum AI Systems): a system-of-systems that spans physical substrates, protocol layers, learning/inference, and cognitive governance—ensuring technical performance aligns with safety and interpretability goals.
Cryptographically Relevant Quantum Computers can undermine classical cryptography and trust anchors under certain assumptions. In this model, CRQC represents the capability vector that elevates attack surface and recovery requirements across stacks.
Large Language Models automate reconnaissance and orchestration: synthesizing exploits, chaining tools, and adapting prompts to evolve attacks—at scales infeasible for manual actors.
The composite CRQC–LLM lens models convergent stress: quantum capability plus AI automation. It provides a structured way to test where architectures fail—and how to instrument them for early detection and recovery.
QALIS (Quantum Artificial Learning & Inference System) is the constructive counter-model. It reframes quantum phenomena as learning primitives and designs for interpretable, auditable pipelines across physical, algorithmic, and cognitive layers.