AA-CDE: Preventing Architectural Collapse in AI and Quantum Systems

AA-CDE (Anti-Arrow Critical Depth Engine) is a stability-budget framework for preventing architectural collapse in complex computational systems, including quantum processors, large-scale AI reasoning pipelines, and hybrid quantum–classical architectures. Modern computational platforms increasingly fail not because of local noise or accuracy degradation, but due to hidden architectural saturation. Routing overhead, correlation density, feedback amplification, and excessive reasoning depth silently exhaust the system’s structural capacity. These failure modes remain invisible to conventional calibration metrics such as coherence time, loss, or validation accuracy. AA-CDE reframes computational depth as a finite, governable stability budget rather than an abstract optimization parameter. It introduces three core observables: Critical Depth Nc — the maximum sustainable effective depth before structural collapse. Stability Fraction f = N_eff / Nc — a normalized architectural load factor. Regime Classification — Stable, Drift, and Breakdown phases describing the global system state. The framework operates at the level of stability phases, segmenting execution trajectories at physical or logical boundaries (measurement, reset, feedback, scheduling epochs) and detecting local saturation events long before global failure becomes observable. This release provides public empirical evidence of a previously unquantified failure mode — the Topology Trap — in which topology-constrained routing causes a non-linear inflation of effective depth, triggering abrupt collapse even when coherence conditions remain nominal. Using a heavy-hex superconducting digital twin and GHZ stress workloads, we demonstrate that stability breakdown is driven by architectural structure rather than physical decoherence. AA-CDE is designed as a portable middleware layer, enabling: topology-aware stability audits for quantum hardware, stability-aware depth policies for QAOA, VQE, QFT, hardware-efficient ansatzes, and error-mitigation workflows, cross-domain expansion into AI reasoning depth governance, distributed training pipelines, edge AI, and complex adaptive systems. This work establishes stability as a first-class architectural resource and provides a foundation for cross-domain stability engineering, enabling computational systems to operate near their expressive limits without crossing irreversible collapse thresholds.