Facial expression recognition on a quantum computer

  title={Facial expression recognition on a quantum computer},
  author={Riccardo Mengoni and Massimiliano Incudini and Alessandra Di Pierro},
  journal={Quantum Machine Intelligence},
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… 

Emotion Quantification Using Variational Quantum State Fidelity Estimation

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

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

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.

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Quantum variational learning for entanglement witnessing

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Benchmarking Small-Scale Quantum Devices on Computing Graph Edit Distance

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

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Quantum Clustering with k-Means: a Hybrid Approach

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.



Implementing a distance-based classifier with a quantum interference circuit

A distance-based classifier that is realised by a simple quantum interference circuit that computes the distance between data points in quantum parallel and is demonstrated using the IBM Quantum Experience and analysed with numerical simulations.

Quantum Computing for Pattern Classification

A quantum pattern classification algorithm is introduced that draws on Trugenberger’s proposal for measuring the Hamming distance on a quantum computer and is discussed using handwritten digit recognition as from the MNIST database.

Quantum Machine Learning

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Transformation of quantum states using uniformly controlled rotations

A unitary transformation which maps any given state of an n-qubit quantum register into another one is considered, which has applications in the initialization of a quantum computer, and also in some quantum algorithms.

Quantum support vector machine for big feature and big data classification

This work shows that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples, and an exponential speedup is obtained.

The theory of variational hybrid quantum-classical algorithms

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The theory of variational hybrid quantum-classical algorithms

The concept of quantum variational error suppression that allows some errors to be suppressed naturally in this algorithm on a pre-threshold quantum device is introduced and the use of modern derivative free optimization techniques can offer dramatic computational savings of up to three orders of magnitude over previously used optimization techniques.

Circuit-Based Quantum Random Access Memory for Classical Data

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The literature in quantum ML is reviewed and perspectives for a mixed readership of classical ML and quantum computation experts are discussed, with particular emphasis on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems.