SIVAC-MAP – Symbiotic AI Validated by Quantum Computation for Autonomous Missions in Prolonged Spaceflight

SIVAC-MAP (Symbiotic AI Validated by Quantum Computation for Autonomous Missions in Prolonged Spaceflight) is a conceptual proposal for a next-generation artificial intelligence framework designed to support astronauts during long-duration deep space missions. Unlike conventional onboard assistants, SIVAC-MAP introduces a modular symbiotic architecture that integrates cognitive supervision, ethical decision-making, explainability, and emotional interaction. The system leverages pre-trained supervised learning models and incorporates a novel quantum validation layer to verify decisions made in isolated or communication-delayed environments. This architecture is intended to enhance astronaut autonomy, reduce cognitive load, and improve mission safety by acting as both an operational assistant and an emotionally adaptive cognitive companion. Potential applications include lunar outposts, Mars transit, and analog mission simulations such as HI-SEAS or HERA. This proposal was developed by Steven Gómez Gutiérrez as an independent research initiative and is open to collaboration, technical evaluation, and further development within the scope of future space exploration programs.