Been deep in QML literature lately and wanted to write up what I actually found vs. what gets hyped. Curious if the community agrees or pushes back.
Where things seem to actually stand:
Barren plateaus are still the core trainability problem. Local cost functions and layerwise training help but don't fully solve it.
QRAM remains the data-loading wall. Without efficient quantum RAM, classical-to-quantum input kills most theoretical speedups before they start.
The one peer-reviewed practical QML advantage I found (early 2026) is Tindall et al. on spatiotemporal chaos prediction in Science Advances. Physics-flavored task, not general ML.
Quantum reservoir computing looks genuinely promising for temporal sequence tasks specifically.
My takeaway: QML has real potential in narrow physics-adjacent tasks but no generic ML advantage yet. The gap between theoretical speedup and practical implementation is still large.
What am I getting wrong? Any recent results I should look at?