Wavefield-Based Interference Structures as a Hardware-Independent Substrate for Quantum-Inspired AI Inference

This preprint proposes a conservative interpretive framework in which quantum algorithm dynamics are analyzed as interference processes within a minimal phenomenological construct termed the Wavefield. Rather than asserting physical equivalence to quantum systems or claiming computational speedup, the work focuses on identifying structural features—such as phase coherence, interference, and measurement-induced suppression—that underlie quantum-inspired computation. The framework further explores how AI systems operating on Wavefield-like representations may reproduce these dynamics and perform constraint-based inverse inference on observed outcomes. This work is intended as a methodological and exploratory proposal, not as a unifying physical theory or a replacement for experimental validation.