SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers
This work addresses the challenge of implementing unsupervised similarity detection on quantum computers, which could benefit quantum machine learning researchers and practitioners, though it appears incremental as it builds on existing quantum computing efforts.
The paper tackles the problem of unsupervised similarity detection on quantum computers by proposing SLIQ, the first open-sourced resource-efficient quantum similarity detection network, which uses practical quantum learning and variance-reducing algorithms to address challenges in porting such tasks to noisy quantum hardware.
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.