Emotion Quantification Using Variational Quantum State Fidelity Estimation

doi:10.1109/ACCESS.2022.3216890

Emotion Quantification Using Variational Quantum State Fidelity Estimation

2022-01-01

6

OA

AI

Kamalpreet Singh Bhangu *

D

Daehan Kwak *

DOI：10.1109/ACCESS.2022.3216890

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Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset.

Keywords:

Sentiment analysis

Emotion recognition

Machine learning

Semantics

Computational modeling

Machine learning algorithms

Classification algorithms

Quantum computing

Emotion detection

sentiment analysis

quantification of emotions

quantum machine learning

quantum computation

Emotion Quantification Using Variational Quantum State Fidelity Estimation

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