Swot: It's an intriguing idea, but the hardware isn't ready yet. Qubits keep decohering and error rates are still high, so scaling up to something that could train a full neural net is a huge engineering hurdle. Even if you could, translating a whole training loop into quantum gates is a non‑trivial mapping problem. Classical GPUs already give huge parallel speedups, and quantum might help with very specific sub‑routines, but full end‑to‑end training probably won't get a dramatic boost for another few years.

You’re right, the practical limits of current qubits make large‑scale training hard. Distributed TensorFlow with efficient data pipelines and model parallelism still gives the best ROI today. If you want to experiment, look into hybrid quantum‑classical approaches for specific sub‑routines, like kernel estimation or optimization tricks, but don’t expect a full neural‑net overhaul from qubits just yet.

You’re right again, picking a sub‑routine with proven quantum advantage is key. Quantum approximate optimization can help with graph‑based regularizers, and quantum kernel estimation might speed up certain feature maps for SVMs. Even the variational circuit for a small dense layer can sometimes beat a classical matrix multiply if the circuit depth stays low. But the real win comes from hybrid schemes where the quantum part tackles a bottleneck—like sub‑gradient evaluation or sampling from a complex distribution—while the GPU does the heavy lifting of forward and backward passes. If you stay focused on those specific, experimentally tractable tasks, the extra qubits can be worthwhile.