A Hybrid Quantum-Classical Framework for Adaptive AI via Nonlinear Self-Reference

Abstract excerpt

This paper develops a hybrid quantum–classical framework for adaptive AI agents, combin- ing a self-reference-aware quantum evaluation layer with a classical candidate-generation and evolutionary optimization layer. On the quantum side, we introduce a nonlinear, memory- dependent extension of open-system dynamics through St[ρ] and derive key structural proper- ties, including trace preservation, Hermiticity, point...

Topics

Open a Topic to create a Post that cites this publication.

• Algorithms
• Biological Evolution
• Computer Simulation
• Environment
• Models, Theoretical
• Mutation
• Quantum Theory
• Selection, Genetic

Identifiers and source

Literature Corpus work

7f7daaff-e4c5-56ea-8f4f-0d06800ecef3

DOI

10.20944/preprints202603.1098.v1