The Curious Similarity Between LLMs and Quantum Mechanics

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Six months ago, I did a deep dive to understand the transformer architecture and noticed something strange: the concepts behind these models strangely mirror many features and phenomena from quantum mechanics. This got me thinking, are transformers proving to be so powerful because they’ve inadvertently captured key design principles of nature, giving them their open-endedness?

Take tokens, for example. Before context clarifies their meaning, they linger in a kind of semantic superposition—like particles existing in multiple states at once. Similarly, self-attention heads bind words across sentences like quantum entanglement, where “he” in one paragraph instantly locks onto “Bob” in another, no matter the distance. Even embedding vectors, those high-dimensional containers of meaning, behave like probability waves that collapse into definite interpretations.

These parallels aren’t perfect (and I’m not claiming transformers literally run on quantum rules). But the overlap feels too coincidental to ignore. Below, I’ve tried to map out the analogies side-by-side (screenshots of tables, because Substack is annoyingly allergic to table formatting). Whether you’re a physicist rolling your eyes or an ML engineer raising an eyebrow, I’d love to hear your thoughts.