SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers

Authors

  • Daniel Silver Northeastern University
  • Tirthak Patel Northeastern University
  • Aditya Ranjan Northeastern University
  • Harshitta Gandhi Northeastern University
  • William Cutler Northeastern University
  • Devesh Tiwari Northeastern University

DOI:

https://doi.org/10.1609/aaai.v37i8.26175

Keywords:

ML: Quantum Machine Learning

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 ef- forts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum com- puters. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum sim- ilarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms.

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Published

2023-06-26

How to Cite

Silver, D., Patel, T., Ranjan, A., Gandhi, H., Cutler, W., & Tiwari, D. (2023). SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers. Proceedings of the AAAI Conference on Artificial Intelligence, 37(8), 9846-9854. https://doi.org/10.1609/aaai.v37i8.26175

Issue

Section

AAAI Technical Track on Machine Learning III