mlsanityData to 14 Oct 2026

SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers

arXiv 2309.15259 ↗First posted 26 Sep 2023quant-ph cs.CV eess.IVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 20 of its 47 references that are in the map. First posted on arXiv 26 Sep 2023.

Builds on

20 of 47 references.

References · 20

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8 more
  1. 2020Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Losscited by 18
  2. 2020Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Networkcited by 17
  3. 2020VSQL: Variational Shadow Quantum Learning for Classificationcited by 17
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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Datacited by 2
  2. 2025ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computerscited by 1
  3. 2024OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machinescited by 0