Researchers have established a clear distinction between the capabilities of low-depth quantum circuits and classical large language models, including transformers and diffusion language models1. This separation is demonstrated in both predictive and generative tasks, highlighting the unique strengths of quantum computation. Specifically, the study shows that quantum circuits can achieve distributional separation, outperforming their classical counterparts in certain scenarios. The implications of this finding are significant, as they underscore the need for urgent planning and migration to post-quantum cryptography. As quantum developments continue to advance, the timeline for cryptographic migration is narrowing, making it essential for practitioners to prioritize PQC planning. The separation between quantum and classical models has substantial consequences for the future of cryptography and cybersecurity, making it crucial for experts to stay ahead of the curve.