Examinando por Autor "Ullah, Ubaid"

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  • Miniatura
    Ítem
    A fully connected quantum convolutional neural network for classifying ischemic cardiopathy
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ullah, Ubaid; García Olea Jurado, Alain; Díez González, Ignacio; García-Zapirain, Begoña
    The prevalence of heart diseases is rising quickly throughout the world, which has an impact on both the world economy and public health. According to the recent statistical survey reports, the increasing mortality rate is due to high blood pressure, high cholesterol, the use of tobacco, obesity, and an inconsistent pulse rate. It is difficult and time-consuming to investigate the various variations of these factors and their impact on Coronary Artery Disease (CAD). Therefore, it is necessary to use modern approaches to diagnose the disease early and minimize the mortality rate. The fields of machine learning and data mining have a wide research dimension and various novel techniques that could help in the prediction of CAD in its early stages and identify their patterns and behaviors in a huge amount of data. The results of such predictions will aid the clinical staff in decision making and early diagnosis. In such a scenario, we proposed a quantum version of the Fully Convolutional Neural Network (FCQ-CNN) for Ischemic Heart Disease (IHD) classification. The proposed model evaluates the quantum circuit-based technique that was inspired by convolutional neural networks, a very successful machine learning model. This method provides O(log (n)) depth for n qubits, reducing the number of parameters and allowing for effective training and testing of real quantum devices. The model has been evaluated by considering the IHD dataset after the data has been cleaned and filtered through the Maximally Relevant Minimally Redundancy (MRMR) filter. For dimension reduction, a Support Vector Machine along with Recursive Feature Elimination (SVM-RFE) has been considered. Initially, the model is tested with 20% of the whole dataset and gets the promising results of a testing accuracy of 84.6% with a testing loss of 0.28. By taking into account the same optimal parameters, the proposed model outcomes are compared to those of the classical Optimized Convolution Neural Network (Optimized-CNN) and Fully Connected Neural Network (FCNN) models. Comparing the model's competency to that of earlier published quantum models yields improvements in accuracy of 8.6%, 12.6%, 3.5%, and 1.8% respectively.
  • Miniatura
    Ítem
    Quantum machine learning approaches applied to healthcare and well-being
    (Universidad de Deusto, 2025-05-06) Ullah, Ubaid; García-Zapirain, Begoña; Madariaga Ortuzar, Aurora; Facultad de Ingeniería
    Quantum computing (QC) differs from traditional computing by using quantum bits (qubits) and leveraging quantum phenomena like superposition, entanglement, and interference. These properties enable quantum computers to perform parallel computations, vastly surpassing classical systems. Quantum machine learning (QML) combines QC with machine learning to enhance data processing and classification. This thesis explores the theoretical advantages and practical applications of QML in healthcare. It begins with a summary of recent advancements, emphasizing QML's significance in the healthcare sector, followed by three key case studies demonstrating its practical use. The study adopts a systematic literature review approach, including research question formulation and article quality assessment using specific metrics. Of 2,038 records collected, 468 duplicates and 1,053 non-related to healthcare were excluded, leaving 55 articles from 2019 to 2024 for evaluation. The analysis provides a foundation for future research, promoting innovative solutions to healthcare challenges at the intersection of quantum computing and machine learning. The first case study examines two QML algorithms Enhanced Quantum Support Vector Machine (E-QSVM) and Quantum Random Forest (QRF) applied to COVID-19 and influenza datasets. After data cleaning, normalization, and scaling, dimensionality is reduced by selecting the top 10 features using Chi-Square, ANOVA, and classical models for quantum efficiency. E-QSVM employs a parameterized quantum circuit with a ZZ feature map to capture complex data relationships, while QRF, implemented via Pennylane, uses two parallel QPUs, selecting the most accurate output. Results show E-QSVM and QRF outperform classical counterparts by 1\% and 2\%, respectively, with E-QSVM and QRF achieving up to 6\% improvement over recent quantum models for the COVID-19 dataset. The second case study introduces a quantum version of the Fully Convolutional Neural Network (FCQ-CNN) for Ischemic Heart Disease (IHD) classification. The model integrates quantum and classical convolutional layers, a pooling layer, and a fully connected layer. Parameters are optimized using the Adam optimizer with a learning rate of 0.001 and a batch size of 32, while binary cross-entropy is used as the loss function over 100 epochs. Dimensionality reduction is achieved using SVM with Recursive Feature Elimination (SVM-RFE). Testing on 20\% of the dataset yields an accuracy of 84.6\% with a loss of 0.28. When compared to classical models (Optimized-CNN and FCNN) and previously published quantum models, the FCQ-CNN shows accuracy improvements of 8.6\%, 12.6\%, 3.5\%, and 1.8\%, respectively. The third case study explores two quantum models: Quantum K Nearest Neighbor (QKNN) and Amplitude Encoding Variational Quantum Classifier (AE-VQC). Both models utilize amplitude encoding to transform input data into quantum state amplitudes and were evaluated on datasets with 8, 16, and 32 features, selected through a feature selection process. QKNN uses the quantum K minimum-finding algorithm to identify the nearest neighbors, measuring distances between quantum states using fidelity and applying a swap test for statistical assessment. AE-VQC comprises a feature map, variational quantum circuit, measurement circuit, and COBYLA optimizer. The experimental results demonstrate that both models outperform their classical counterparts, with QKNN achieving 3\%, 3\%, and 0.5\% higher accuracy across set of features. AE-VQC also shows improved accuracy of 0.3\% and 0.5\% in classifying sarcopenia disease, while reducing computational complexity. In summary, this thesis evaluates various QML models, including data pre-processing and state preparation, across three diffrent case studies involving COVID-19, cardiovascular disease, and sarcopenia. The models show improved accuracy over classical machine learning techniques, highlighting QML's potential in addressing complex healthcare challenges efficiently. Despite these challenges like noise and limited resources, the findings contribute to the growing intersection of QML and healthcare, offering promising avenues for future research.
  • Miniatura
    Ítem
    Quantum machine learning revolution in healthcare: a systematic review of emerging perspectives and applications
    (Institute of Electrical and Electronics Engineers Inc., 2024) Ullah, Ubaid; García-Zapirain, Begoña
    Quantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique properties exhibited by subatomic particles, such as superposition, entanglement, and interference. These quantum phenomena enable quantum computers to operate on an entirely different level, exponentially surpassing the computational capabilities of classical computers. By manipulating qubits and capitalising on their quantum states, QC holds the promise of solving complex problems that are currently intractable in the case of traditional computers. The potential impact of QC extends beyond its computational power and reaches into various critical sectors, including healthcare. Scientists and engineers are working diligently to overcome various challenges and limitations associated with QC technology. These include issues related to qubit stability, error correction, scalability, and noise reduction. In such a scenario, our proposed work provides a concise summary of the most recent state of the art based on articles published between 2018 and 2023 in the healthcare domain. Additionally, the approach follows the necessary guidelines for conducting a systematic literature review. This includes utilising research questions and evaluating the quality of the articles using specific metrics. Initially, a total of 2,038 records were acquired from multiple databases, with 468 duplicate records and 1,053 records unrelated to healthcare subsequently excluded. A further 258, 68, and 39 records were eliminated based on title, abstract, and full-text criteria, respectively. Ultimately, the remaining 49 articles were subject to evaluation, thus providing a brief overview of the recent literature and contributing to existing knowledge and comprehension of Quantum Machine Learning (QML) algorithms and their applications in the healthcare sector. This analysis establishes a foundational framework for forthcoming research and development at the intersection of QC and machine learning, ultimately paving the way for innovative approaches to addressing complex challenges within the healthcare domain