# Facial expression recognition on a quantum computer

@article{Mengoni2021FacialER, title={Facial expression recognition on a quantum computer}, author={Riccardo Mengoni and Massimiliano Incudini and Alessandra Di Pierro}, journal={Quantum Machine Intelligence}, year={2021}, volume={3}, pages={1-11} }

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…

## 8 Citations

### Emotion Quantification Using Variational Quantum State Fidelity Estimation

- Computer ScienceIEEE Access
- 2022

The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime and successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset.

### Quantum median filter for total variation image denoising

- PhysicsANNALI DELL'UNIVERSITA' DI FERRARA
- 2022

In this new computing paradigm, named quantum computing, researchers from all over the world are taking their first steps in designing quantum circuits for image processing, through a difficult…

### Parallel Quantum Computation Approach for Quantum Deep Learning and Classical-Quantum Models

- Computer Science, PhysicsJournal of Physics: Conference Series
- 2021

This work presents encouraging results of how it is possible to use Quantum Processing Units analogically to Graphics Processing Units to accelerate algorithms and improve the performance of machine learning models through three experiments and proposes an alternative as a proof of concept to address emotion recognition problems using optimization algorithms and how execution times can be positively affected by parallel quantum computation.

### Computing graph edit distance on quantum devices

- Computer ScienceQuantum Mach. Intell.
- 2022

This paper presents a QUBO formulation of the GED problem, which allows to implement two different approaches, namely quantum annealing and variational quantum algorithms, that run on the two types of quantum hardware currently available: quantumAnnealer and gate-based quantum computer, respectively.

### Quantum variational learning for entanglement witnessing

- Computer Science, Physics2022 International Joint Conference on Neural Networks (IJCNN)
- 2022

This work made use of Quantum Neural Networks (QNNs) in order to successfully learn how to reproduce the action of an entanglement witness, and may pave the way to an efficient combination of QML algorithms and quantum information protocols, possibly outperforming classical approaches to analyse quantum data.

### Benchmarking Small-Scale Quantum Devices on Computing Graph Edit Distance

- Computer ScienceArXiv
- 2021

This paper presents a comparative study of two quantum approaches to computing GED: quantum annealing and variational quantum algorithms, which refer to the two types of quantum hardware currently available, namely quantumAnnealer and gate-based quantum computer, respectively.

### Towards graph classification with Gaussian Boson Sampling by embedding graphs on the X8 photonic chip

- Computer Science
- 2021

This technical report studies the generated samples of the graph embedding method which leads to feature vectors and tries to take photon loss into account and explain the observed results accordingly.

### Quantum Clustering with k-Means: a Hybrid Approach

- Computer ScienceArXiv
- 2022

This paper designs, implements, and evaluates three hybrid quantum k-Means algorithms, exploiting quantum phenomena to speed up the computation of distances, and shows that these algorithms can be more e-cient than the classical version, still obtaining comparable clustering results.

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