Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks
Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods remains an open question, particularly beyond the context of specifically crafte...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10884820/ |
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| author | Diego Alvarez-Estevez |
| author_facet | Diego Alvarez-Estevez |
| author_sort | Diego Alvarez-Estevez |
| collection | DOAJ |
| description | Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods remains an open question, particularly beyond the context of specifically crafted toy problems, and given the current limitations of quantum hardware. This study focuses on quantum kernel methods in the context of classification tasks. In particular, it examines the performance of quantum kernel estimation and quantum kernel training (QKT) in connection with two quantum feature mappings, namely, ZZFeatureMap and CovariantFeatureMap. Remarkably, these feature maps have been proposed in the literature under the conjecture of possible near-term quantum advantage and have shown promising performance in ad hoc datasets. This study aims to evaluate their versatility and generalization capabilities in a more general benchmark, encompassing both artificial and established reference datasets. Classical machine learning methods, specifically support vector machines and logistic regression, are also incorporated as baseline comparisons. Experimental results indicate that quantum methods exhibit varying performance across different datasets. Despite outperforming classical methods in ad hoc datasets, mixed results are obtained for the general case among standard classical benchmarks. The experimental data call into question a general added value of applying QKT optimization, for which the additional computational cost does not necessarily translate into improved classification performance. Instead, it is suggested that a careful choice of the quantum feature map in connection with proper hyperparameterization may prove more effective. |
| format | Article |
| id | doaj-art-e24245f670f54887a719d384a84293c3 |
| institution | DOAJ |
| issn | 2689-1808 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-e24245f670f54887a719d384a84293c32025-08-20T02:54:18ZengIEEEIEEE Transactions on Quantum Engineering2689-18082025-01-01611510.1109/TQE.2025.354188210884820Benchmarking Quantum Machine Learning Kernel Training for Classification TasksDiego Alvarez-Estevez0https://orcid.org/0000-0001-5790-0577CITIC Research Center, Universidade da Coruña, A Coruña, SpainQuantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods remains an open question, particularly beyond the context of specifically crafted toy problems, and given the current limitations of quantum hardware. This study focuses on quantum kernel methods in the context of classification tasks. In particular, it examines the performance of quantum kernel estimation and quantum kernel training (QKT) in connection with two quantum feature mappings, namely, ZZFeatureMap and CovariantFeatureMap. Remarkably, these feature maps have been proposed in the literature under the conjecture of possible near-term quantum advantage and have shown promising performance in ad hoc datasets. This study aims to evaluate their versatility and generalization capabilities in a more general benchmark, encompassing both artificial and established reference datasets. Classical machine learning methods, specifically support vector machines and logistic regression, are also incorporated as baseline comparisons. Experimental results indicate that quantum methods exhibit varying performance across different datasets. Despite outperforming classical methods in ad hoc datasets, mixed results are obtained for the general case among standard classical benchmarks. The experimental data call into question a general added value of applying QKT optimization, for which the additional computational cost does not necessarily translate into improved classification performance. Instead, it is suggested that a careful choice of the quantum feature map in connection with proper hyperparameterization may prove more effective.https://ieeexplore.ieee.org/document/10884820/Benchmarkingquantum kernel estimation (QKE)quantum kernel training (QKT), quantum machine learning (QML) |
| spellingShingle | Diego Alvarez-Estevez Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks IEEE Transactions on Quantum Engineering Benchmarking quantum kernel estimation (QKE) quantum kernel training (QKT), quantum machine learning (QML) |
| title | Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks |
| title_full | Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks |
| title_fullStr | Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks |
| title_full_unstemmed | Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks |
| title_short | Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks |
| title_sort | benchmarking quantum machine learning kernel training for classification tasks |
| topic | Benchmarking quantum kernel estimation (QKE) quantum kernel training (QKT), quantum machine learning (QML) |
| url | https://ieeexplore.ieee.org/document/10884820/ |
| work_keys_str_mv | AT diegoalvarezestevez benchmarkingquantummachinelearningkerneltrainingforclassificationtasks |