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Journal of Advanced Computational Intelligence and Intelligent Informatics

Bloch Sphere-Based Representation for Quantum Emotion Space

Fei Yan, Abdullah M. Iliyasu, Zhentao Liu, Ahmed S. Salama, Fangyan Dong, Kaoru Hirota

Published Jan 20, 2015• 18 citations
Publisher

Abstract

A Bloch Sphere-based Emotion Space (BSES), where two angles φ and θ in the Bloch sphere represent the emotion (such as happiness, surprise, anger, sadness, expectation, or relaxation in [0, 2π)) and its intensity (from neutral to maximum in [0, π]), respectively, is proposed. It exploits the psychological interpretation of color to assign a basic color to each emotion subspace such that the BSES can be visualized, and by using quantum gates, changes in emotions can be tracked and recovered. In an experimental validation, two typical human emotions, happiness and sadness, are analyzed and visualized using the BSES according to a preset emotional transmission model. A transition matrix that tracks emotional change can be used to control robots allowing them to adapt and respond to human emotions.

DOI
10.20965/jaciii.2015.p0134

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A Bloch Sphere-based Emotion Space (BSES), where two angles φ and θ in the Bloch sphere represent the emotion (such as happiness, surprise, anger, sadness, expectation, or relaxation in [0, 2π)) and its intensity (from neutral to maximum in [0, π]), respectively, is proposed. It exploits the psychological interpretation of color to assign a basic color to each emotion subspace such that the BSES can be visualized, and by using quantum gates, changes in emotions can be tracked and recovered. In an experimental validation, two typical human emotions, happiness and sadness, are analyzed and visualized using the BSES according to a preset emotional transmission model. A transition matrix that tracks emotional change can be used to control robots allowing them to adapt and respond to human emotions.
In the design of artificially sentient robots, an obstacle always has been that conventional computers cannot really process information in parallel, whereas the human affective system is capable of producing experiences of emotional concurrency (e.g., happy and sad). Another schism that has been in the way is the persistent Cartesian divide between cognition and affect, whereas people easily can reflect on their emotions or have feelings about a thought. As an essentially theoretical exercise, we posit that quantum physics at the basis of neurology explains observations in cognitive emotion psychology from the belief that the construct of reality is partially imagined (Im) in the complex coordinate space C^3. We propose a quantum computational account to mixed states of reflection and affect, while transforming known psychological dimensions into the actual quantum dynamics of electromotive forces. As a precursor to actual simulations, we show examples of possible robot behaviors, using Einstein-Podolsky-Rosen circuits. Keywords: emotion, reflection, modelling, quantum computing
Utilising the properties of quantum mechanics, i.e., entanglement, parallelism, etc., a quantum structure is proposed for representing and manipulating emotion space of robots. This quantum emotion space (QES) provides a mechanism to extend emotion interpretation to the quantum computing domain whereby fewer resources are required and, by using unitary transformations, it facilitates easier tracking of emotion transitions over different intervals in the emotion space. The QES is designed as an intuitive and graphical visualisation of the emotion state as a curve in a cuboid, so that an “emotion sensor” could be used to track the emotion transition as well as its manipulation. This ability to use transition matrices to convey manipulation of emotions suggests the feasibility and effectiveness of the proposed approach. Our study is primarily influenced by two developments. First, the massive amounts of data, complexity of control, planning and reasoning required for today’s sophisticated automation processes necessitates the need to equip robots with powerful sensors to enable them adapt and operate in all kinds of environments. Second, the renewed impetus and inevitable transition to the quantum computing paradigm suggests that quantum robots will have a role to play in future data processing and human-robot interaction either as standalone units or as part of larger hybrid systems. The QES proposed in this study provides a quantum mechanical formulation for quantum emotion as well as a platform to process, track, and manipulate instantaneous transitions in a robot’s emotion. The new perspective will open broad areas, such as applications in emotion recognition and emotional intelligence for quantum robots.
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.
Emotional cognition is nonlinear, probabilistic, and interdependent, making it hard to recreate human emotional intelligence in AI systems. The study offers the Quantum-Emotion Neural Network (QENN). This unique Quantum Physics-Inspired AI approach uses aspects of quantum mechanics such as superposition, entanglement, and quantum probability amplitudes in neural network design. Where regular models handle emotions as non-interacting or unchanging parts, QENN represents them using amplitude encoding and quantum-inspired neurons to allow all emotional states to be managed together. Training and evaluation of the model took place on the IndiComics Emotion (ICE) dataset, which has 17,340 multimodal instances over eight emotion classes-happy, Sad, Angry, Fearful, Disgust, Surprised, Neutral, and Mixed emotions. The tests showed QENN achieved better results than CNN and LSTM, with an accuracy of 93.8%, 93.5%\mathbf{9 3. 5 \%}93.5% precision, 92.9%\mathbf{9 2. 9 \%}92.9% recall, an F1\mathbf{F 1}F1-score of 93.2%\mathbf{9 3. 2 \%}93.2%, and an ROC-AUC of 95.8%\mathbf{9 5. 8 \%}95.8% overall. In contrast, traditional CNN and LSTM models had 84.7% and 85.3% accuracies, and their ROC-AUC was 87.2% and 88.5%, respectively. Evaluation with the confusion matrix showed that precision remained above 90%\mathbf{9 0 \%}90% in all emotion types, even for complex ones like ‘Mixed.’ Also, the training-validation accuracy curves revealed excellent generalisation and limited overfitting, with a learning rate of 0.001 giving the most minor validation loss, equal to 0.21. These outcomes show that quantum-inspired solutions are robust and easy to understand for emotion modelling. This work proves that QENN is a critical step forward in making machines that understand emotions as people do.
This study presents a modest attempt to interpret, formulate, and manipulate emotion of robots within the precepts of quantum mechanics. Our proposed framework encodes the emotion information as a superposition state whilst unitary operators are used to manipulate the transition of the emotion states which are recovered via appropriate quantum measurement operations. The framework described provides essential steps towards exploiting the potency of quantum mechanics in a quantum affective computing paradigm. Further, the emotions of multi-robots in a specified communication scenario are fused using quantum entanglement thereby reducing the number of qubits required to capture the emotion states of all the robots in the environment, and fewer quantum gates are needed to transform the emotion of all or part of the robots from one state to another. In addition to the mathematical rigours expected of the proposed framework, we present a few simulation-based demonstrations to illustrate its feasibility and effectiveness. This exposition is an important step in the transition of formulations of emotional intelligence to the quantum era.
Quantum computing is an emerging field with great potential, yet its abstract mathematical nature makes it difficult to understand. This study proposes an interactive Bloch sphere teaching system based on MQTT, designed to create an intuitive learning tool for quantum education. The system allows students to visualize quantum state transformations through the rotation of a physical pointer corresponding to quantum gate operations. It can serve as an educational human–machine interface (HMI), enabling learners to directly interact with quantum concepts through intuitive visual feedback and the motion of the physical sphere and pointer. The framework is built on a Message Queuing Telemetry Transport (MQTT) API implemented in Node-RED on a Raspberry Pi, allowing multiple HMI devices to communicate via MQTT to control the physical Bloch sphere. By integrating HMI design with educational objectives, this system transforms abstract quantum mechanics into tangible experiences, fostering students’ curiosity, interactivity, and engagement in quantum learning.