In the Brain-Computer Interfaces where Electroencephalogram (EEG) signals are used, fast and accurate analysis of data is one of the important steps. For this aim, various machine learning methods are being developed. In this study, we compared the performance of the Quantum Powered Support Vector Machine (QSVM) with classical machine learning algorithms for classifying EEG data. Analyzes were performed in the quantum simulation environment and due to the inadequacies in the quantum environment, the number of samples in the data set was reduced by using Principal Component Analysis. The maximum performance of QSVM was found to be 99.47% in the experiments. When all the results in the analyzes are examined, it is seen that the QSVM has achieved good performance. By removing the limitations of quantum computers and making changes in the hyperparameters of quantum-based algorithms, the use of quantum-based machine learning methods in the classification of EEG data can provide better results in terms of accuracy and runtime.
In the Brain-Computer Interfaces where Electroencephalogram (EEG) signals are used, fast and accurate analysis of data is one of the important steps. For this aim, various machine learning methods are being developed. In this study, we compared the performance of the Quantum Powered Support Vector Machine (QSVM) with classical machine learning algorithms for classifying EEG data. Analyzes were performed in the quantum simulation environment and due to the inadequacies in the quantum environment, the number of samples in the data set was reduced by using Principal Component Analysis. The maximum performance of QSVM was found to be 99.47% in the experiments. When all the results in the analyzes are examined, it is seen that the QSVM has achieved good performance. By removing the limitations of quantum computers and making changes in the hyperparameters of quantum-based algorithms, the use of quantum-based machine learning methods in the classification of EEG data can provide better results in terms of accuracy and runtime.
Research using innovative, rapidly evolving quantum-based systems is becoming increasingly important. The acceleration provided by the real use of a few quantum computers in the world constitutes very important steps for the computer world. Quantum-based systems are also frequently used in data science and machine learning. In this study, Quantum Support Vector Machine (QSVM), a quantum-based machine learning algorithm, is compared with classical machine learning algorithms. IBM - Studies on quantum computer simulation have been carried out. The data dimension is reduced by first applying Principal Component Analysis (PCA) to the dataset. Afterwards, analyzes were made and the results were presented comparatively. The highest performance of the QSVM algorithm was obtained at 92.3%. Compared to classical machine learning algorithms, the performance of the QSVM algorithm is slightly lower. It is thought that higher performance and speed will be achieved in QSVM by eliminating limitations such as limited qubit resources and system restrictions. It is clear that the results found are promising for quantum-based machine learning algorithms.
Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be effectively utilized in the field of healthcare.
Quantum computing is an emerging field that can effectively solve several machine learning problems. The immensely growing data size has begun to create barriers for classical machine learning algorithms. Quantum computers proficiently handle and process big data composed of metrics and vectors. This paper presents the quantum machine learning model based on quantum support vector machines (QSVM) to classify brain tumours into malignant and benign. The dataset used in this experiment is Brats 2015, available on Kaggle. The QSVM-based model was run on real-time quantum machines and simulators to extract the performance metrics in terms of accuracy and execution time. The QSVM classification model is compared with its classical SVM equivalent. The kernel-based QSVM model implementation on a 32-qubit quantum simulator was 188 times faster with 95% accuracy which is 1.60% better than the classical equivalent. Similarly, the model takes 24.19% less time than the classical SVM model when realized on a 5-qubit real-time superconducting processor with the same accuracy. Hence, the results reveal that quantum machine learning models implemented on quantum computers outperformed their classical equivalents.
In recent years, quantum technology has been developing in several fields, including quantum computing. Quantum Computing (QC) is a type of computing that uses quantum bits (qubits) instead of classical bits to perform calculations. Unlike classical computers, which use binary logic (0s and 1s), quantum computers exploit quantum phenomena (such as superposition and entanglement), which are expected to bring advantages over traditional Machine Learning (ML). However, QC is still an emerging field, and further research is needed to fully understand its potential in ML. In this regard, for example, it is still unclear how QC compares with traditional ML in terms of stability and generalization. Starting from this consideration, this work intends to experimentally compare quantum and traditional ML. In particular, the classical Support Vector Machine (SVM), which belongs to the traditional ML approach, and a Quantum Support Vector Machine (QSVM), as Quantum Machine Learning (QML) approach (which combines quantum computing and machine learning through quantum properties), are applied to a specific case study related to electroencephalographic (EEG) data. The comparison was conducted on similar development environments to ensure comparable results from a measurement standpoint. The results highlight that there are some critical points involving the generalization ability of QML algorithms, possibly related to an appropriate and specific hyperparameter search.
The electrocardiogram (ECG) is the most common technique used to diagnose heart diseases. The electrical signals produced by the heart are recorded by chest electrodes and by the extremity electrodes placed on the limbs. Many diseases, such as arrhythmia, cardiomyopathy, coronary heart disease, and heart failure, can be diagnosed by examining ECG signals. The interpretation of these signals by experts may take a long time, and there may be differences between expert interpretations. Since technological developments are intertwined with the medical sciences, computer-assisted diagnostic methods have recently come forward. In computer science, machine learning techniques are often preferred for automatic detection. Quantum-based structures have emerged to increase the machine learning algorithm's speed and classification performance. In this study, a quantum-based machine learning algorithm is applied to classify heart rhythms. The ECG properties were converted to qubit structure using principal component analysis (PCA). The resulting qubits are classified using the quantum support vector machine (QSVM) algorithm. Quantum computer simulation over Qiskit was used for classification studies. Within the scope of experimental studies, comparisons between classical SVM and QSVM were made using different data amounts and qubit numbers. In the results of the analysis, classical SVM achieved 86.96% accuracy, and QSVM achieved 84.64% accuracy. Despite the fact that the entire dataset was not used due to various limitations, these successful performances were achieved. Classification of medical data such as that from ECG has shown that quantum-based machine learning frameworks perform well despite current resource constraints. In this respect, the study includes essential contributions to the use of quantum-based machine learning methods on signal data in medicine.
A brain-computer interface (BCI) system enables direct communication between the brain and external devices, offering significant potential for assistive technologies and advanced human-computer interaction. Despite progress, BCI systems face persistent challenges, including signal variability, classification inefficiency, and difficulty adapting to individual users in real time. In this study, we propose a novel hybrid quantum learning model, termed QSVM-QNN, which integrates a Quantum Support Vector Machine (QSVM) with a Quantum Neural Network (QNN), to improve classification accuracy and robustness in EEG-based BCI tasks. Unlike existing models, QSVM-QNN combines the decision boundary capabilities of QSVM with the expressive learning power of QNN, leading to superior generalization performance. The proposed model is evaluated on two benchmark EEG datasets, achieving high accuracies of 0.990 and 0.950, outperforming both classical and standalone quantum models. To demonstrate real-world viability, we further validated the robustness of QNN, QSVM, and QSVM-QNN against six realistic quantum noise models, including bit flip and phase damping. These experiments reveal that QSVM-QNN maintains stable performance under noisy conditions, establishing its applicability for deployment in practical, noisy quantum environments. Beyond BCI, the proposed hybrid quantum architecture is generalizable to other biomedical and time-series classification tasks, offering a scalable and noise-resilient solution for next-generation neurotechnological systems.