Traditional machine learning algorithms often struggle with large datasets and complex computations. Quantum algorithms, on the other hand, can process information in parallel due to the principles of superposition and entanglement. This parallelism allows QML to solve certain problems exponentially faster than classical algorithms, making it particularly useful for the computationally intensive tasks in nanotechnology.
