Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913558598516736 |
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| author | Sahin, M. Emre Symons, Benjamin C. B. Pati, Pushpak Minhas, Fayyaz Millar, Declan Gabrani, Maria Mensa, Stefano Robertus, Jan Lukas |
| author_facet | Sahin, M. Emre Symons, Benjamin C. B. Pati, Pushpak Minhas, Fayyaz Millar, Declan Gabrani, Maria Mensa, Stefano Robertus, Jan Lukas |
| contents | Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel have been developed, allowing the kernel to be trained and therefore aligned with a specific dataset. While quantum kernel alignment is a promising technique, it has been hampered by considerable training costs because the full kernel matrix must be constructed at every training iteration. Addressing this challenge, we introduce a novel method that seeks to balance efficiency and performance. We present a sub-sampling training approach that uses a subset of the kernel matrix at each training step, thereby reducing the overall computational cost of the training. In this work, we apply the sub-sampling method to synthetic datasets and a real-world breast cancer dataset and demonstrate considerable reductions in the number of circuits required to train the quantum kernel while maintaining classification accuracy. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_02879 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training Sahin, M. Emre Symons, Benjamin C. B. Pati, Pushpak Minhas, Fayyaz Millar, Declan Gabrani, Maria Mensa, Stefano Robertus, Jan Lukas Quantum Physics Machine Learning Quantum machine learning with quantum kernels for classification problems is a growing area of research. Recently, quantum kernel alignment techniques that parameterise the kernel have been developed, allowing the kernel to be trained and therefore aligned with a specific dataset. While quantum kernel alignment is a promising technique, it has been hampered by considerable training costs because the full kernel matrix must be constructed at every training iteration. Addressing this challenge, we introduce a novel method that seeks to balance efficiency and performance. We present a sub-sampling training approach that uses a subset of the kernel matrix at each training step, thereby reducing the overall computational cost of the training. In this work, we apply the sub-sampling method to synthetic datasets and a real-world breast cancer dataset and demonstrate considerable reductions in the number of circuits required to train the quantum kernel while maintaining classification accuracy. |
| title | Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2401.02879 |