The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems. The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body. Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind. One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables, which provide more data density due to its exponential state-space growth. These hidden variables correspond to the amplitudes of each probable state of the system and allow for the modeling of various intricate aspects of measurable and observable physical quantities. This makes the quantum wave-functions powerful and felicitous to model cognitive states of the human mind, as it inherits the ability to efficiently couple the current context with past experiences temporally and spatially to approach an appropriate future cognitive state. This paper implements and compares some techniques like Variational Quantum Classifiers (VQC), quantum annealing classifiers, and hybrid quantum-classical neural networks, to harness the power of quantum computing for processing cognitive states of the mind by making use of EEG data. It also introduces a novel pipeline by logically combining some of the aforementioned techniques, to predict future cognitive responses. The preliminary results of these approaches are presented and are very encouraging with upto 61.53% validation accuracy.
The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems. The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body. Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind. One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables, which provide more data density due to its exponential state-space growth. These hidden variables correspond to the amplitudes of each probable state of the system and allow for the modeling of various intricate aspects of measurable and observable physical quantities. This makes the quantum wave-functions powerful and felicitous to model cognitive states of the human mind, as it inherits the ability to efficiently couple the current context with past experiences temporally and spatially to approach an appropriate future cognitive state. This paper implements and compares some techniques like Variational Quantum Classifiers (VQC), quantum annealing classifiers, and hybrid quantum-classical neural networks, to harness the power of quantum computing for processing cognitive states of the mind by making use of EEG data. It also introduces a novel pipeline by logically combining some of the aforementioned techniques, to predict future cognitive responses. The preliminary results of these approaches are presented and are very encouraging with upto 61.53% validation accuracy.
Applications in behavioural research, human-computer interaction, and mental health depend on the ability to recognize emotions. In order to improve the accuracy of emotion recognition using electroencephalography (EEG) data, this work presents a hybrid quantum deep learning technique. Conventional EEG-based emotion recognition techniques are limited by noise and high-dimensional data complexity, which make feature extraction difficult. To tackle these issues, our method combines traditional deep learning classification with quantum-enhanced feature extraction. To identify important brain wave patterns, Bandpass filtering and Welch method are used as preprocessing techniques on EEG data. Intricate inter-band interactions that are essential for determining emotional states are captured by mapping frequency band power attributes (delta, theta, alpha, and beta) to quantum representations. Entanglement and rotation gates are used in a hybrid quantum circuit to maximize the model's sensitivity to EEG patterns associated with different emotions. Promising results from evaluation on a test dataset indicate the model's potential for accurate emotion recognition. The model will be extended for real-time applications and multi-class categorization in future study, which could improve EEG-based mental health screening instruments. This method offers a promising tool for applications in adaptive human-computer systems and mental health monitoring by showcasing the possibilities of fusing traditional deep learning with quantum processing for reliable, scalable emotion recognition.
Sleep staging is an important task in the field of medicine and is significant for diagnosing sleep disorders and assessing overall health. Classical machine learning has made significant progress in this field, and with the development of quantum computing, quantum machine learning (QML) provides a new model for solving complex data classification tasks. In this paper, we propose a hybrid quantum-classical neural network (HQCNN) architecture based on electroencephalogram (EEG) single-channel signals for automating the sleep staging task. The classical part uses a convolutional neural network (CNN) and a long short-term memory network (LSTM) to extract features of the EEG signal, while the quantum part utilizes variational quantum circuit (VQC) for feature mapping and classification. We evaluated the model on different datasets. The results show that the model is satisfactory for classification. This indicates that the combination of quantum computing and machine learning has great potential and provides new research directions and technical approaches for developing more efficient computational models and dealing with complex data problems.
The entwining of quantum computing with classical machine learning has proven to be one of the most promising frontiers of artificial intelligence, as it possesses the potential to speed up intricate computations in ways that classical systems will never be able to achieve. This study delves into the theoretical and practical implications of hybrid quantum-classical approaches in machine learning, while also examining their advantages, limitations, and applicability in different domains. Utilizing quantum entanglement and parallelism, hybrid solutions also have the potential to process high-dimensional data, optimize complex functions, and facilitate pattern recognition tasks more efficiently. However, noise, decoherence, and limited qubit connectivity on current hardware pose considerable barriers to practical implementations. The authors discuss hybrid architectures like quantum-enhanced optimizers and variational quantum circuits which include classical pre- and post-processing to alleviate quantum limitations. Moreover, a comparative study is performed to show the effectiveness of hybrid models comparing with classical machine learning state-of-the-art in natural language processing, computer vision and financial modeling. Results indicate that although hybrid quantum–classical approaches provide significant gains in optimization and computational efficiency; these benefits remain limited by hardware limitations and the scalability of quantum algorithms. With these considerations in mind, the paper offers guidance for targeting future research directions in quantum machine learning, including advances in quantum error correction, quantum hardware scalability, and new algorithmic approaches. Additional advancements needed and notably, the study focused on a smaller representation of data Since quantum computing is relatively new and researchers are still exploring its application, it is still early days in terms of machine learning models that are solely, intrinsically quantum.
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, primarily affecting the elderly population and leading to significant cognitive decline. This decline manifests in various mental faculties such as attention, memory, and higher-order cognitive functions, severely impacting an individual's ability to comprehend information, acquire new knowledge, and communicate effectively. One of the tasks influenced by cognitive impairments is handwriting. By analyzing specific features of handwriting, including pressure, velocity, and spatial organization, researchers can detect subtle changes that may indicate early-stage cognitive impairments, particularly AD. Recent developments in classical artificial intelligence (AI) methods have shown promise in detecting AD through handwriting analysis. However, as the dataset size increases, these AI approaches demand greater computational resources, and diagnoses are often affected by limited classical vector spaces and feature correlations. Recent studies have shown that quantum computing technologies, developed by harnessing the unique properties of quantum particles such as superposition and entanglement, can not only address the aforementioned problems but also accelerate complex data analysis and enable more efficient processing of large datasets. In this study, we propose a variational quantum classifier with fewer circuit elements to facilitate early AD diagnosis based on handwriting data. Our model has demonstrated comparable classification performance to classical methods and underscores the potential of quantum computing models in addressing cognitive problems, paving the way for future research in this domain.
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.
This thesis explores the integration of quantum computing into Machine Learning (ML), aiming to evaluate the effectiveness and limitations of quantum-enhanced models compared to classical approaches. As quantum technologies continue to develop, they offer the potential for solving complex computational problems more efficiently, particularly in fields requiring high-dimensional data processing. A literature review is conducted to contextualize current advancements in Quantum Machine Learning, highlighting theoretical benefits and existing practical limitations. This work investigates whether quantum methods, specifically the Quantum Support Vector Classifier (QSVC), can provide measurable improvements over its classical counterpart. The QSVC, implemented using IBM’s Qiskit ML library, replaces traditional kernel functions with quantum kernels computed via quantum circuits and feature maps, embedding classical data into a Hilbert space. The experimental portion of this thesis compares classical and quantum models on two distinct datasets: one from the healthcare domain and another from particle physics. Various parameters are tested in a constrained grid search due to the high computational demands of quantum circuits. Results indicate that while quantum models are significantly more resource-intensive, they can achieve comparable or even improved performance on data that has more complex relations between their variables. However, scalability remains a major challenge. This work concludes that although QSVC does not yet consistently outperform classical models, it offers advantages in specific contexts and lays the groundwork for identifying future use cases where quantum computing may offer a practical advantage in ML.