From Bayesian Belief to Generative Behavior: Uncertainty-Preserving Quantum Bayesian Feedback under Evolving Evidence

Generative systems under incomplete or changing evidence must decide both which conditions to express and how frequently to express them. The challenge is to translate uncertainty over alternative conditions into observable generative behavior. We introduce Quantum Bayesian Feedback (QBF), which maintains a persistent joint belief over task hypotheses and observation noise, recursively updates it with new evidence, and maps the resulting belief to a distribution over controls for a frozen generator. We show that QBF belief updates are equivalent to Bayesian filtering, derive bounds characterizing uncertainty preservation through readout and generation, and quantify information lost under point-valued readouts. Experiments with pretrained diffusion models demonstrate that posterior differences not captured by posterior-mean or maximum a posteriori (MAP) readouts remain reflected in generated regional attributes under posterior sampling. Target-switching experiments further depict how belief revision translates into adaptive changes in generative response across candidate sets. Extending QBF to real-image evidence and native $512\times 512$ semantic generation further reveals trade-offs among accuracy, stability, and recovery under different belief-update dynamics. Measured by the mean energy score, QBF achieves 31.6–34.3% lower regional generation loss than point-valued readouts and semantic scores close to those of adaptive feedback baselines, while preserving posterior distinctions and adapting to target changes. Ideal quantum simulations show that the belief dynamics are induced directly by QBF state evolution and readout. Overall, QBF translates evolving posterior uncertainty into adaptive generative behavior under changing evidence, from regional attribute control to semantic high-resolution generation.