A divide-and-conquer algorithm for quantum state preparation

TL;DR

This paper introduces a divide-and-conquer quantum state preparation algorithm that significantly reduces the circuit depth needed to load high-dimensional data, enabling more efficient quantum machine learning applications.

Contribution

It presents a novel polylogarithmic depth quantum circuit for data loading, leveraging ancillary qubits and a divide-and-conquer approach, improving over previous linear-depth methods.

Findings

Successfully demonstrated on a real quantum device

Achieves efficient data loading with reduced circuit depth

Enables potential quantum speedups in data-intensive tasks

Abstract

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.