# Facial Expression Recognition on a Quantum Computer

arXiv: https://arxiv.org/abs/2102.04823 · HTML: https://mlsanity.com/paper/2102.04823/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 9 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Quantum support vector machine for big data classification](https://mlsanity.com/paper/1307.0471/) (2013)

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

- [Quantum Clustering with k-Means: a Hybrid Approach](https://mlsanity.com/paper/2212.06691/) (2022)
- [Computing Graph Edit Distance with Algorithms on Quantum Devices](https://mlsanity.com/paper/2111.10183/) (2021)
- [Quantum median filter for Total Variation image denoising](https://mlsanity.com/paper/2212.01041/) (2022)

## Abstract (arXiv)

We address the problem of facial expression recognition and show a possible solution using a quantum machine learning approach. In order to define an efficient classifier for a given dataset, our approach substantially exploits quantum interference. By representing face expressions via graphs, we define a classifier as a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states. We discuss the accuracy of the quantum classifier evaluated on the quantum simulator available on the IBM Quantum Experience cloud platform, and compare it with the accuracy of one of the best classical classifier.