  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.
