QSTAR: Short Physical Prefixes for Terminal-Risk Prediction and Conditional Layout Sampling

A layout that looks physically plausible can still fail a second later, when a stack settles, slides, and falls. A generator that must respect such constraints needs a world model, but only one answer from it: the probability that the constraint still holds at the horizon, calibrated well enough to serve as a sampling weight. We introduce QSTAR ( Quantum Sampling Through Anticipated Responses), which obtains this answer by short physical imagination: an exact simulator runs for the first 0.25 s of a 1.5 s horizon, and a gradient-boosted head learns only the continuation of the terminal event. The predicted probabilities reweight a prior over candidate layouts into one conditional distribution. Sampling it is a rejection-sampling problem, solved exactly either by classical normalization or by fixed-point amplitude amplification, whose acceptance guarantee needs only the lower bound that weight softening supplies; we bound how score, sampler, and readout errors reach any downstream decoder. On 96 held-out contact-stack scenes, QSTAR reduces Brier error by 46% (three objects) and 36% (four objects) relative to a strong static predictor, uses 4.14 times less compute than full simulation, and keeps over 90% of the gain when only 10.4% of initial states are simulated. The error decreases in all 14 paired comparisons, covering new scenes, velocity shifts, sparse querying, and four further dynamical systems. All 48 compiled circuits accept with probability above 0.99 and reproduce the classical distribution to $10^{-14}$, and a frozen diffusion editor preserves the selected layout in 95.6% of 768 images. As static selection is already near its ceiling here, the main benefit is calibrated risk at low simulation cost rather than higher top-choice survival.