Tensor Network Based Efficient Quantum Data Loading of Images

TL;DR

This paper introduces a scalable quantum data loading method for images using tensor networks, enabling efficient amplitude encoding suitable for near-term quantum computers and demonstrated on real hardware.

Contribution

A novel tensor network-based quantum state preparation method that scales logarithmically with image size and is experimentally validated on a trapped ion quantum computer.

Findings

Efficient quantum image encoding with logarithmic resource scaling.

Successful experimental demonstration on 8-qubit trapped ion quantum computer.

First large-scale amplitude encoding of complex images in quantum states.

Abstract

Image-based data is a popular arena for testing quantum machine learning algorithms. A crucial factor in realizing quantum advantage for these applications is the ability to efficiently represent images as quantum states. Here we present a novel method for creating quantum states that approximately encode images as amplitudes, based on recently proposed techniques that convert matrix product states to quantum circuits. The numbers of gates and qubits in our method scale logarithmically in the number of pixels given a desired accuracy, which make it suitable for near term quantum computers. Finally, we experimentally demonstrate our technique on 8 qubits of a trapped ion quantum computer for complex images of road scenes, making this the first large instance of full amplitude encoding of an image in a quantum state.