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 exponential time advantage using 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.

State preparation algorithms are routines used in many quantum computing applications, from solving a linear system of equations to quantum machine learning. In this paper, we present an exact state preparation algorithm that considers entanglement to reduce the complexity of the generated quantum circuit. The proposed method allows approximation by entanglement reduction. The approximation of an entangled state by two separable states allows a quadratic decrease in the number of CNOT gates in exchange for an approximation error. Through experimental analysis with 7-qubits devices, the approximation error observed through measurements is smaller than the error due to the noise generated by the circuit to prepare the original state.

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