Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets
The Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called “Pegasus.” This groundbreaking system opens new possibilities for solving highly complex problems. The advancement of quantum annealers...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10845761/ |
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| author | Hadi Salloum Kamil Sabbagh Vladislav Savchuk Ruslan Lukin Osama Orabi Marat Isangulov Manuel Mazzara |
| author_facet | Hadi Salloum Kamil Sabbagh Vladislav Savchuk Ruslan Lukin Osama Orabi Marat Isangulov Manuel Mazzara |
| author_sort | Hadi Salloum |
| collection | DOAJ |
| description | The Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called “Pegasus.” This groundbreaking system opens new possibilities for solving highly complex problems. The advancement of quantum annealers has spurred experimental demonstrations and intensified research interest, particularly in quantum machine learning. However, the application of quantum annealing in machine learning remains limited due to the lack of conclusive performance evaluations on real-world datasets. There is still no clear consensus on the efficacy of these models in practical scenarios. This work focuses on experimentally evaluating quantum annealing machine learning (QAML) classification methods, specifically Quantum Support Vector Machines (QSVM) and QBoost, on ADMET datasets—one of the most important datasets in the drug discovery domain. We compare QAML with classical machine learning to evaluate their relative performance. This study seeks to address this gap by rigorously comparing the performance of QBoost and QSVM models using ADMET datasets, employing D-Wave’s Quantum Annealer. This work provides a comprehensive analysis of the potential and limitations of quantum annealers in quantum machine learning, with a focus on their application to real-world data in the ADMET domain. The findings offer critical insights into the nuanced advantages and challenges of quantum annealers in advancing machine learning methodologies. |
| format | Article |
| id | doaj-art-eb56833ea6eb4f098c4e568795551091 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eb56833ea6eb4f098c4e5687955510912025-01-29T00:01:11ZengIEEEIEEE Access2169-35362025-01-0113162631628710.1109/ACCESS.2025.353139110845761Performance of Quantum Annealing Machine Learning Classification Models on ADMET DatasetsHadi Salloum0https://orcid.org/0009-0005-6068-0532Kamil Sabbagh1https://orcid.org/0009-0004-1003-8472Vladislav Savchuk2Ruslan Lukin3Osama Orabi4https://orcid.org/0009-0000-0028-315XMarat Isangulov5Manuel Mazzara6https://orcid.org/0000-0002-3860-4948Research Center of the Artificial Intelligence Institute, Innopolis University, Innopolis, RussiaResearch Center of the Artificial Intelligence Institute, Innopolis University, Innopolis, RussiaResearch Center of the Artificial Intelligence Institute, Innopolis University, Innopolis, RussiaResearch Center of the Artificial Intelligence Institute, Innopolis University, Innopolis, RussiaDepartment of Computer Science and Engineering, Innopolis University, Innopolis, RussiaResearch Center of the Artificial Intelligence Institute, Innopolis University, Innopolis, RussiaDepartment of Computer Science and Engineering, Innopolis University, Innopolis, RussiaThe Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called “Pegasus.” This groundbreaking system opens new possibilities for solving highly complex problems. The advancement of quantum annealers has spurred experimental demonstrations and intensified research interest, particularly in quantum machine learning. However, the application of quantum annealing in machine learning remains limited due to the lack of conclusive performance evaluations on real-world datasets. There is still no clear consensus on the efficacy of these models in practical scenarios. This work focuses on experimentally evaluating quantum annealing machine learning (QAML) classification methods, specifically Quantum Support Vector Machines (QSVM) and QBoost, on ADMET datasets—one of the most important datasets in the drug discovery domain. We compare QAML with classical machine learning to evaluate their relative performance. This study seeks to address this gap by rigorously comparing the performance of QBoost and QSVM models using ADMET datasets, employing D-Wave’s Quantum Annealer. This work provides a comprehensive analysis of the potential and limitations of quantum annealers in quantum machine learning, with a focus on their application to real-world data in the ADMET domain. The findings offer critical insights into the nuanced advantages and challenges of quantum annealers in advancing machine learning methodologies.https://ieeexplore.ieee.org/document/10845761/Quantum computingquantum annealingPegasus topologyquantum support vector machines (QSVM)QBoostADMET datasets |
| spellingShingle | Hadi Salloum Kamil Sabbagh Vladislav Savchuk Ruslan Lukin Osama Orabi Marat Isangulov Manuel Mazzara Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets IEEE Access Quantum computing quantum annealing Pegasus topology quantum support vector machines (QSVM) QBoost ADMET datasets |
| title | Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets |
| title_full | Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets |
| title_fullStr | Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets |
| title_full_unstemmed | Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets |
| title_short | Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets |
| title_sort | performance of quantum annealing machine learning classification models on admet datasets |
| topic | Quantum computing quantum annealing Pegasus topology quantum support vector machines (QSVM) QBoost ADMET datasets |
| url | https://ieeexplore.ieee.org/document/10845761/ |
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