Editing Quantum Machine Learning (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ QML vs. Classical ML Honest Comparison (NISQ era)
! Aspect !! QML (NISQ) !! Classical ML
|-
| Problem size || Very small (<50 features) || Very large (millions of features)
|-
| Training speed || Slow (quantum overhead + noise) || Fast (GPU-optimized)
|-
| Demonstrated advantage || None conclusive yet || Established across many tasks
|-
| Error rates || High (NISQ noise) || Minimal (deterministic)
|-
| Hardware cost || Very high (quantum chips) || Low (GPUs)
|-
| Chemistry simulation || Promising (few molecules) || Exponentially hard at scale
|}

'''Failure modes''': Barren plateaus — gradient vanishing with circuit depth makes deep QNNs untrainable. Noise accumulation — NISQ hardware errors grow exponentially with circuit depth. Dequantization — many proposed quantum advantages have been classically matched. Classical simulation — small enough quantum circuits to run on NISQ hardware can often be efficiently simulated classically, eliminating the need for quantum hardware.
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