Abstract In this paper, the study represents a theoretical conceptualization on the potential improvements to interpret and devise under the notion of quantum mechanics & AI utilizing psychology for social robots. The framework is elaborated in regard to the development of emotions encoded through information and possibilities, which manipulates the use of transitions of these emotions in states through the Quantum & AI measurements. To support the development, work introduced is an interpretation of quantum mechanics, linked with quantum science, systems science, and Conceptualized Neural Network. The understanding of these emotions are expected to impact the technical capability of robots in a specific aspect to sense, capture, and act in an environment relied on optimization of quantum bits, where a robot, perform these emotions through a network which will allow interaction with the given target, changing the robot’s state of response according to optimization and gates from target itself, computing the robot’s emotional performance based on measurement and mathematical expressions changing in accordance to the available information. This method will help to emphasize its viability and effectiveness in the synthesis of emotional intellect in social robots.
Abstract In this paper, the study represents a theoretical conceptualization on the potential improvements to interpret and devise under the notion of quantum mechanics & AI utilizing psychology for social robots. The framework is elaborated in regard to the development of emotions encoded through information and possibilities, which manipulates the use of transitions of these emotions in states through the Quantum & AI measurements. To support the development, work introduced is an interpretation of quantum mechanics, linked with quantum science, systems science, and Conceptualized Neural Network. The understanding of these emotions are expected to impact the technical capability of robots in a specific aspect to sense, capture, and act in an environment relied on optimization of quantum bits, where a robot, perform these emotions through a network which will allow interaction with the given target, changing the robot’s state of response according to optimization and gates from target itself, computing the robot’s emotional performance based on measurement and mathematical expressions changing in accordance to the available information. This method will help to emphasize its viability and effectiveness in the synthesis of emotional intellect in social robots.
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.
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.
A future quantum technological infrastructure demands the development of quantum cyber-physical-cognitive systems, merging quantum artificial intelligence, quantum robotics and quantum information and communication technologies. To support such a development, the current work introduces a new interpretation of quantum mechanics, grounded on a link between quantum computer science, systems science and field-based computation. This new interpretation is applied to quantum artificial neural networks, with examples implemented experimentally on IBM's five qubit transmon bowtie chip, accessed via cloud using IBM Q Experience, illustrating how quantum neural computing can be implemented on actual quantum computers. A new form of quantum neural machine learning, based on a quantum optimization of a conditional utility function is also introduced and applied to quantum robotics, where a quantum robot, characterized by an interface and a multilayer quantum artificial neural network, interacts with a quantum target, changing the target's dynamics adaptively, based upon the quantum optimization dynamics, computing the optima for a performance measure and changing the target's dynamics accordingly.
Loneliness and isolation are on the rise worldwide, threatening human well-being and the wellness of different age groups and backgrounds. Notably, global social distancing measures during the COVID-19 crisis have exacerbated this problem, resulting in various psychological and physiological ailments. Within both the categories of social and medical robots, companion robots are capable of engaging emotionally with users and providing continuous monitoring and assessment of their health. In this study, we propose a framework for modeling the emotion space of companion robots to facilitate their emotion generation and transition based on Plutchik’s wheel of emotions and reversible quantum circuit schemes. Superposition encodings allow fewer computing resources for the generation and storage of emotional states, and by using unitary operations, they facilitate easier emotion transition and recovery over different intervals. Further, an encryption strategy is designed based on the emotion communication architecture to secure the emotion-related data in human-robot interaction. It is hoped that such an integrative framework and research agenda exploring the role of companion robots will be useful to care for users’ social health by mitigating their negative emotions, especially during difficult times.
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% precision, 92.9% recall, an F1-score of 93.2%, and an ROC-AUC of 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% 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.
Quantum computing is typically associated with domains such as cybersecurity, drug discovery, finance, and large-scale algorithmic optimization. While these applications drive technological progress, they remain largely disconnected from the emotional and psychological realities that shape everyday human experience. To enhance personal relevance, quantum systems can instead be applied to support daily emotional well-being, guiding individuals through complex affective states and fostering more harmonious, enriched lives. This study proposes an AI framework that leverages quantum optimization to interpret and classify emotional expressions from text, expanding computational capacity to understand aspects of human consciousness and contextual affect in synchrony with lived experience. By embedding quantum-enhanced affective computation into daily interactions, the model moves beyond predictive analytics to form a dynamic interface with emotional cognition, with potential applications in counselling, self-awareness, and therapeutic support across clinical and organizational settings. Using the HappyDB dataset, containing over 100,000 self-reported happy moments annotated with emotional categories, we demonstrate that conventional AI approaches struggle to capture subtle context and interdependent emotional cues. Our method addresses this gap by integrating classical neural architectures with a quantum variational layer, enabling synchronous evaluation of complex semantic and affective relationships in text. The hybrid model transforms text into semantic embeddings, processes them through sequential LSTM layers, and optimizes latent emotional representations via quantum-inspired techniques. This allows the system to emulate human-like context awareness and achieve enhanced emotion classification beyond the capabilities of classical methods. By applying quantum computing to daily-life emotional cognition at scale, this work reframes quantum algorithms as tools for enriching human experience. Beyond mental health assessment and digital therapeutics, it establishes a new frontier for human-centered quantum intelligence, demonstrating that complex psychological and semantic interactions can be optimized to advance AI-assisted well-being.