The Hidden Bottleneck in Quantum Machine Learning: Getting Data into a Quantum Computer

Quantum Machine Learning (QML) promises powerful computing advantages, but there's a major hidden problem: quantum computers cannot directly read classical data. Before any computation begins, data must be converted into quantum states — a process that can be exponentially costly. While amplitude encoding stores data compactly using far fewer qubits, actually preparing those states requires enormous computational effort, potentially canceling out any quantum advantage entirely.