# SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers

arXiv: https://arxiv.org/abs/2309.15259 · HTML: https://mlsanity.com/paper/2309.15259/

## 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 (most cited first, 20 of 20)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Quantum advantage in learning from experiments](https://mlsanity.com/paper/2112.00778/) (2021)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Reinforcement Learning with Quantum Variational Circuits](https://mlsanity.com/paper/2008.07524/) (2020)
- [Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems](https://mlsanity.com/paper/1911.11071/) (2019)
- [Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Loss](https://mlsanity.com/paper/2008.04378/) (2020)
- [Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network](https://mlsanity.com/paper/2002.02814/) (2020)
- [VSQL: Variational Shadow Quantum Learning for Classification](https://mlsanity.com/paper/2012.08288/) (2020)
- [Personalized Hashtag Recommendation for Micro-videos](https://mlsanity.com/paper/1908.09987/) (2019)
- [Toward Physically Realizable Quantum Neural Networks](https://mlsanity.com/paper/2203.12092/) (2022)
- [Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks](https://mlsanity.com/paper/2205.04051/) (2022)
- [Binary Classifier Inspired by Quantum Theory](https://mlsanity.com/paper/1903.01167/) (2019)
- [Generalized quantum similarity learning](https://mlsanity.com/paper/2201.02310/) (2022)

## Built on by (most cited first, 3 of 3)

- [EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data](https://mlsanity.com/paper/2503.14473/) (2025)
- [ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers](https://mlsanity.com/paper/2506.21537/) (2025)
- [OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machines](https://mlsanity.com/paper/2409.19823/) (2024)

## 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.