CRQC–LLM™: A Stress-Test Lens for Quantum AI Systems
This page provides the canonical, citable definition of CRQC–LLM™ and its role in evaluating resilience boundaries for Quantum AI Systems (QAIS) under cryptographic pressure and adversarial automation.
Canonical Definition
Definition: CRQC–LLM is a stress-test architectural lens for Quantum AI Systems that models how cryptographically relevant quantum computation, when combined with automated adversarial reasoning, pressures coherence governance, verification placement, synchronization discipline, and trust boundaries in hybrid deployments.
Abstract
CRQC–LLM frames an adversarial evaluation regime for Quantum AI Systems in which cryptographically relevant quantum capability is treated as a system-level stressor rather than a purely computational milestone. The lens examines how capability escalation interacts with automation, inference manipulation, and governance failure modes—especially across measurement interfaces and distributed synchronization. The outcome is a structured view of where QAIS designs become brittle and which verification controls must be staged to preserve trust.
Keywords: cryptographic pressure, adversarial automation, verification staging, coherence governance, synchronization drift, hybrid deployment, resilience boundaries
1. Motivation
In QAIS, capability and constraint co-emerge. As quantum capability becomes cryptographically relevant, the threat model shifts: classical trust assumptions can fail, and automated reasoning systems can amplify adversarial pressure at machine speed. CRQC–LLM provides a disciplined framework to evaluate those pressures at the architectural level.
2. Stress Surfaces
CRQC–LLM highlights recurring failure surfaces that must be handled as first-class architectural variables:
- Verification placement across quantum–classical interfaces (measurement as a trust boundary).
- Synchronization drift in distributed or hybrid pipelines (timing becomes a security parameter).
- Coherence governance (runtime budget violations manifest as integrity loss).
- Cryptographic transition pressure (assurance regimes must evolve with capability).
- Automation-amplified adversaries (LLM-assisted probing, prompt-level manipulation, policy exploitation).
3. Evaluation Method
CRQC–LLM is applied as a review discipline: define threat assumptions, map trust boundaries, identify verification checkpoints, then evaluate how adversarial automation would exploit weak interfaces or coherence budget overruns. The goal is not only to identify vulnerabilities, but to enforce design rules that keep resilience aligned with capability.
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4. Relationship to QALIS
CRQC–LLM is intentionally paired with QALIS. QALIS answers how to construct capability under physical constraint; CRQC–LLM answers how capability fails when cryptographic pressure and adversarial automation stress the system. Together they operationalize constructive design and stress-tested resilience as a single discipline.
References
[1] Dr. Joe Wilson, Quantum AI Systems: Architectures for Artificial Intelligence. QuSciTech Press. (forthcoming / in preparation).
[2] QuSciTech, “CRQC–LLM: Defined Term,” quscitech.com (this page).
[3] Shor, P. W., “Algorithms for quantum computation: discrete logarithms and factoring,” (foundational work motivating CRQC framing).
Cite This Page
Wilson, J., “CRQC–LLM: A Stress-Test Lens for Quantum AI Systems,” QuSciTech (Defined Term), accessed .