Mehdi Saeedi

When Close Enough Is Not Enough: Autoregressive Drift in Quantum Circuit Synthesis

The contrast between settings is the central finding: when approximate outputs can be rescued by post-processing, the transformer succeeds; when exact discrete correctness is required, autoregressive drift limits reliability, with both inference-time search and data scaling as effective levers while training-side fine-tuning and model-level diversification are not.