  Advantages in several fields of research and industry are expected with the
rise of quantum computers. However, the computational cost to load classical
data in quantum computers can impose restrictions on possible quantum speedups.
Known algorithms to create arbitrary quantum states require quantum circuits
with depth O(N) to load an N-dimensional vector. Here, we show that it is
possible to load an N-dimensional vector with a quantum circuit with
polylogarithmic depth and entangled information in ancillary qubits. Results
show that we can efficiently load data in quantum devices using a
divide-and-conquer strategy to exchange computational time for space. We
demonstrate a proof of concept on a real quantum device and present two
applications for quantum machine learning. We expect that this new loading
strategy allows the quantum speedup of tasks that require to load a significant
volume of information to quantum devices.

Quantum Physics;Machine Learning