Trivian Relational Architecture Beyond Classical Computation: A Compatibility Analysis and Research Agenda for Hybrid AI–Quantum Systems

Hybrid AI–quantum workflows distribute computation, interpretation, and action across systems with different execution conditions and authority boundaries, a result can remain technically valid even after the context or authorization supporting its use has changed. This paper develops a philosophical and architectural account of those transitions through the Trivian Relational Architecture, arguing that governing heterogeneous computation requires attention to the relationships through which evidence is interpreted, authority is delegated, and decisions become consequential. It introduces a contextual, multi-role ontology distinguishing participants, computational resources, environmental interfaces, and governance authorities, then maps the five existing Trivian components (Relational Field Constants, Syzygy Rosetta, Coheronmetry, Orthogonal Signal, and the Trivian Resonance Lattice) to hybrid workflows — developing extensions for intent-bounded delegation, execution-linked provenance, resource-bounded authority, and action-time eligibility. A logistics scenario and an accompanying failure taxonomy illustrate specific risks including interpretation inflation, stale authority, computational authority laundering, ineffective human oversight, and categorical overgeneralization. Revocable participation and participant-specific trust are proposed to preserve both the right to refuse and the possibility of differentiated relationship. The paper closes with a comparative research agenda connecting the proposal to classical testbeds, quantum simulation, and targeted hardware studies, specifying matched baselines, independent outcomes, and explicit overhead limits. The contribution is a conceptual integration and evaluation framework, no new experimental results are reported. It establishes a basis for examining how relational governance can remain intelligible and accountable across changing computational substrates, while leaving its empirical value open to comparison and revision.