Abstract

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.

Highlights

  • Emotional intelligence is primarily concerned with the capacity of an entity to exhibit, control, and express emotions and the ability to handle interpersonal relationships judiciously and empathetically

  • We propose a reformulated emotion space but as a prelude to that, we pause to ponder over two questions: (1) what are the quantum mechanical properties that could support effective expression of emotion? and (2) how does this “quantumness” relate to the description of a quantum robot?

  • From the layout of the quantum emotion space (QES) presented in the preceding sections, we see that eight qubits are required to construct the robot’s QES model; for this example, out of this number, four qubits are used to encode the information about emotion (i.e., QPA plane) and four qubits are employed to encode the temporal information about the emotion transition

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Summary

Introduction

Emotional intelligence is primarily concerned with the capacity of an entity (human or robot) to exhibit, control, and express emotions and the ability to handle interpersonal (i.e., human-human, human-robot, or robot-robot) relationships judiciously and empathetically. Proposed the concepts that apply quantum circuits to model fuzzy sets, thereby creating a new method to model emotional behaviors for a humanoid robot [10] This new method makes use of the entanglement and partial measurement properties from quantum mechanics available to the modeling of fuzzy systems to evolve behaviours of humanoid robots with a genetic algorithm [11]. These works are treated as the pioneering results in quantum emotion studies, neither of them provides a specific mathematical formulation to describe the quantum emotion and its space. Integrating quantum mechanics into descriptions of emotion space supports the development of qubots as well as its use to manipulate classical robot devices to perform advanced emotion-related actions [20]. We complete the section by presenting a well-thought-out example that demonstrates execution of the whole QES process

Quantum Bits and Quantum Gates
Modified PA Plane
Quantum Version of PA Plane
Representation for QES
Emotion Transition Matrix
Procedure of Quantum Emotion Transition
Quantum Emotion Initialisation and Transition
Example to Illustrate Quantum Emotion Manipulation
Quantum Emotion Retrieval from QES
Concluding remarks
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