The Generative Reality Hypothesis: A Computational Framework for Quantum Mechanics

We propose the Generative Reality Hypothesis (GRH), a novel interpretation of quantum mechanics that reconceptualizes physical reality as an information-processing system analogous to generative artificial intelligence models. Under this framework, quantum superposition represents the latent space of possible states, wave function collapse corresponds to sampling from probability distributions during information queries (measurements), and the Born rule emerges as the fundamental sampling algorithm of reality. We present rigorous mathematical foundations establishing formal equivalences between quantum dynamics and specific generative models. Our framework recasts the measurement problem as a question of computational process, explains non-local correlations as the enforcement of consistency within a shared generative model, and provides a mechanistic explanation for wave-particle duality as a transition between two computational modes: continuous latent evolution (wave) and discrete sampling (particle). We propose five specific, falsifiable experimental tests using current quantum technologies that can distinguish GRH from standard quantum mechanics through quantitative predictions about complexity-dependent decoherence, measurement dynamics, and information flow. Statistical criteria for falsification are established with required experimental precision specified. The framework connects quantum mechanics to information theory and computational complexity, offering a computationally grounded foundation for quantum phenomena while making testable predictions that differ measurably from existing interpretations.