What question did this study set out to answer?

This research aims to establish a framework for ensuring safety in hybrid quantum-classical AI systems by defining fundamental structural constraints.

May 22, 2026Open Access

A Compositional Information-Theoretic Framework for Hybrid Quantum–Classical AI Safety

Key Points

This research aims to establish a framework for ensuring safety in hybrid quantum-classical AI systems by defining fundamental structural constraints.
Introduced three structural constraints: thermodynamic entropy budgets, epistemic influence bounds, and structural independence requirements.
Developed candidate compositional bounds and identified necessary proof obligations for system-level safety.
Outlined a five-layer reference stack for runtime monitoring and certification architecture.
Defined candidate compositional bounds that provide a basis for future system-level guarantees.
Described a certification workflow sketch to enhance assurance in hybrid AI systems.
Identified technical requirements to support validation based on domain-specific criteria.

Abstract

603 — AxoDen-Q FoundationsThis paper presents an early AxoDen-Q research note for hybrid quantum-classical AI safety. It proposes three structural constraints: thermodynamic entropy budgets, epistemic influence bounds, and structural independence requirements. The document formulates candidate compositional bounds and identifies proof obligations needed to move from local stage constraints to system-level guarantees. It is a foundational research note, not an implementation guide. 604 — AxoDen-Q Systems & CertificationThis engineering companion interprets the AxoDen-Q safety envelope as runtime and certification architecture. It outlines a five-layer reference stack, monitoring targets, VSR-Q recovery patterns, CI/CD and policy-as-code controls, and a certification workflow sketch for high-assurance systems. Claims of correctness or assurance are design targets contingent on the companion theory and domain-specific validation. 605 — AxoDen-Q Technical AppendixThis technical appendix develops the broader compositional information-theoretic framework for hybrid classical and quantum AI safety. It covers EBDP, EFI, Q-EBDP, Q-EFI, compositional entropy bounds, model-influence constraints, fabrication-detection statistics, variational reconciliation, estimation approaches, certification protocols, runtime monitoring, and representative domain applications. Any case-study material should be read as protocol structure rather than measured deployment performance.

A Compositional Information-Theoretic Framework for Hybrid Quantum–Classical AI Safety

Key Points

Abstract

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