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International Conference on Intelligent Technologies (CONIT)

From Neural Dynamics to Emotional Intelligence: A Quantum Physics-Inspired AI Framework Bridging Psychological Minds and Machine Learning Models

Gurpreet Kaur, Kavita Kavita, Bhanvi Wadhawan, Sapna Dhiman, Pronoy Chopra, Loveleena Mukhija

Published Jun 20, 2025• 1 citation
Publisher

Abstract

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%, $\mathbf{9 3. 5 \%}$ precision, $\mathbf{9 2. 9 \%}$ recall, an $\mathbf{F 1}$-score of $\mathbf{9 3. 2 \%}$, and an ROC-AUC of $\mathbf{9 5. 8 \%}$ 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 $\mathbf{9 0 \%}$ 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.

DOI
10.1109/conit65521.2025.11167315

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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.
Abstract This study explores Quantum AI’s potential in enhancing Speech Emotion Recognition (SER) systems. Our primary objective is to evaluate the performance of quantum-inspired algorithms compared to classical machine-learning approaches in accurately identifying and classifying emotions from speech signals. We hypothesise that quantum computing techniques can improve the efficiency and accuracy of emotion recognition, particularly in handling complex, highdimensional acoustic data. We developed a hybrid quantum-classical model that combines quantum-inspired neural networks with traditional feature extraction techniques. Our approach utilises quantum circuits for data encoding and quantum variational algorithms for classification. We implemented quantum versions of support vector machines (QSVM) and quantum approximate optimisation algorithms (QAOA) for emotion classification tasks. These quantum methods were compared against a hybrid Convolutional Neural Network and Long Short Term (LSTM). The hybrid network is called a Convolutional Long Short Term network(CLSTM). The study employed a custom dataset for speech emotion recognition. We prepossessed the audio data to extract relevant acoustic features, including mel-frequency cepstral coefficients (MFCCs), pitch, and energy. In contrast, our implemented quantum-inspired model for this corpus showed lower performance, with the highest training accuracy of 30%, struggling with most emotions but performing best with ’Trust’. These varied results across different implementations suggest that while Quantum AI techniques show promise in advancing speech emotion recognition, their effectiveness may depend on the specific dataset, language, and model architecture used. Finally, the speed of a simulated quantum network is shown to outperform current methods in terms of matrix multiplication by leaps and bounds.
In the field of People Intelligence, Emotion Analytics is one of the emerging and growing challenges. A prominent study field is analyzing an individual's emotional state from textual data, as well as recognizing emotions from audio and video recordings. Current Artificial Intelligence approaches for Emotion Analytics based on machine and deep learning and neural networks based on classic data science approaches assume rational people's decision-making process. People's decision-making is irrational. As a result of recent quantum cognition advancements, we show that emotional judgments from one modality may be incompatible with judgments from another, and they cannot be assessed together to produce a final judgment. As a result, the cognitive process exhibits "quantum-like" biases that classical AI approaches based on probability models challenged to analyze. As a result, we offer an emotion analytics approach based on the quantum data science method for predicting people's emotions by a fundamentally novel assessment method.
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.
Emotion Recognition (ER) is an important capability that is increasingly being used to enhance the user experience in various fields such as, Human-Computer interaction, Cognitive Neuroscience, Neurofeedback, Robotics, Artificial Intelligence, Augmented Reality, Virtual Reality, Mental Health, etc. Emotions are generated in the limbic system, a set of brain structures that are involved in emotional processing, memory, and motivation. ER from electroencephalography (EEG) signals has gained attention from researchers in recent years as it provides a more accurate and non-intrusive way of understanding human emotions. This research paper presents a comprehensive study of the use of Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) for ER using EEG signals. The aim of this study is to investigate the potential of quantum machine learning algorithms in improving the performance of ER tasks compared to classical Machine Learning (ML) algorithms. The model was simulated using IBM Qiskit due to the limited resources of quantum computers. Intriguingly, the experimental results contradict the speed-up claims of Quantum Computing (QC) by exhibiting longer computation times and lesser accuracy when compared to traditional methods. SVM and NN yield 68.5% and 69.2% accuracy while the quantum version gives 63% and 65.2% accuracy on the benchmark dataset SEED. This research provides evidence that Quantum Machine Learning (QML) algorithms need to be improved for ER from EEG signals and provide a reliable and non-intrusive way of understanding human emotions.
Emotion recognition in speech is essential for human-computer interaction. This study introduces a refined hybrid Quantum Convolutional Neural Network (QCNN) framework designed to substantially enhance Speech Emotion Recognition (SER). It addresses major challenges such as speaker variability, noise robustness, and cross-domain applicability that often limit classical deep learning models. By Utilizing quantum principles like superposition and entanglement within a 6-qubit, 8-layer parameterized circuit, the model derives highdimensional, noise-resistant features from speech signals. The method includes sophisticated preprocessing steps, such as bandpass filtering and energy-based Voice Activity Detection (VAD), followed by Mel-Frequency Cepstral Coefficients (MFCC) extraction. Evaluated on a diverse dataset of 4,515 samples across eight emotions (Angry, Calm, Disgust, Fear, Happy, Neutral, Sad, Surprised), the QCNN achieved an accuracy of 86 %, setting a new benchmark. It surpasses classical approaches such as a CNN with attention(77%)(77 \%)(77%), a traditional QCNN(77.87%)(77.87 \%)(77.87%), and a hybrid quantum-classical network(76%)(76 \%)(76%). The model demonstrated balanced performance, with precision scores ranging from 0.79 to 1.0 (including a perfect 1.0 for Fear) and recall scores from 0.82 to 0.94. These results underscore the promise of quantum-enhanced neural networks in capturing complex, nonlinear speech emotion patterns and establishing a new standard for robust, generalizable SER systems.
Quantum computing has emerged as a breakthrough technology for sentiment and emotion analysis, providing computational efficiency and interpretability benefits. This paper compares three quantum-inspired methods: fully complex-valued neural networks, multimodal sentiment analysis models, and quantum affective computing. This paper compares three quantum-inspired methods: fully complex-valued neural networks, multimodal sentiment analysis models, and quantum affective computing models. The research uses six benchmark sentiment datasets (CR, MPQA, MR, SST-2, SUBJ, and SST-5) to compare the performance of the methods. The approach involves representing textual and multimodal data as complex quantum states, using quantum-inspired feature representations, and using quantum measurement methods for classification. The results show that the Complex Quantum Neural Network (QNN) performs better than baseline models such as BERT and RoBERTa, with the best F1-scores on all datasets. Specifically, it has an F1-score of 86 on CR, 86 on MPQA, 90 on MR, 88 on SST-2, 97 on SUBJ, and 53 on SST-5, outperforming baseline deep learning methods. These results show the potential of quantum computing to revolutionize sentiment analysis, providing more efficient and interpretable models with better contextual understanding and scalability.