Quantum Image Loading: Hierarchical Learning and Block-Amplitude Encoding
Abstract
Given the excitement for the potential of quantum computing for machine learning methods, a natural subproblem is how to load classical data into a quantum state. Leveraging insights from [GST24] where certain qubits play an outsized role in the amplitude encoding, we extend the hierarchical learning framework to encode images into quantum states. We successfully load digits from the MNIST dataset as well as road scenes from the Honda Scenes dataset. Additionally, we consider the use of block amplitude encoding, where different parts of the image are encoded in a tensor product of smaller states. The simulations and overall orchestration of workflows was done on the BlueQubit platform. Finally, we deploy our learned circuits on both IBM and Quantinuum hardware and find that these loading circuits are sufficiently shallow to fit within existing noise rates.
Cite
@article{arxiv.2504.10592,
title = {Quantum Image Loading: Hierarchical Learning and Block-Amplitude Encoding},
author = {Hrant Gharibyan and Hovnatan Karapetyan and Tigran Sedrakyan and Pero Subasic and Vincent P. Su and Rudy H. Tanin and Hayk Tepanyan},
journal= {arXiv preprint arXiv:2504.10592},
year = {2025}
}