SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers

TL;DR

SLIQ introduces a resource-efficient quantum similarity detection network designed for noisy quantum computers, leveraging quantum learning and variance reduction to enable unsupervised similarity tasks.

Contribution

It is the first open-source quantum similarity detection network that is practical and effective for noisy quantum hardware.

Findings

Demonstrates feasibility of quantum similarity detection on noisy quantum computers

Utilizes variance-reducing algorithms to improve quantum learning performance

Provides an open-source implementation for further research

Abstract

Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these efforts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum computers. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum similarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms.