Looped language models (LoopLMs) increase effective computational depth by repeatedly applying shared Transformer blocks without increasing the parameter count. However, this reuse makes them particularly sensitive to post-training quantization (PTQ). We systematically study PTQ in LoopLMs and identify three sources of degradation: distribution shift across roles, state reuse across loop transitions, and recursive error accumulation. We propose LoopQ, a loop-aware PTQ framework that preserves a shared quantized backbone while introducing lightweight adaptations. LoopQ combines loop-aware activation scaling and selective transformations to accommodate changing hidden-state distributions, cross-loop state alignment to stabilize transitions, and trajectory-aware calibration to limit accumulated error. Under W4A4 quantization, LoopQ achieves a 67.8% average relative improvement in five-task mean accuracy across four backbones and a conservative estimate of 75.2% for the average relative perplexity reduction across eight backbone-dataset pairs, compared with the strongest static PTQ baseline for each comparison.

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