SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers

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.

References in corpus (5)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers
• Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Loss
• Toward Physically Realizable Quantum Neural Networks
• Generalized quantum similarity learning