Pulsar Classification: Comparing Quantum Convolutional Neural Networks and Quantum Support Vector Machines
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910591876071424 |
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| author | Slabbert, Donovan Lourens, Matt Petruccione, Francesco |
| author_facet | Slabbert, Donovan Lourens, Matt Petruccione, Francesco |
| contents | Well-known quantum machine learning techniques, namely quantum kernel assisted support vector machines (QSVMs) and quantum convolutional neural networks (QCNNs), are applied to the binary classification of pulsars. In this comparitive study it is illustrated with simulations that both quantum methods successfully achieve effective classification of the HTRU-2 data set that connects pulsar class labels to eight separate features. QCNNs outperform the QSVMs with respect to time taken to train and predict, however, if the current NISQ era devices are considered and noise included in the comparison, then QSVMs are preferred. QSVMs also perform better overall compared to QCNNs when performance metrics are used to evaluate both methods. Classical methods are also implemented to serve as benchmark for comparison with the quantum approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_15592 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Pulsar Classification: Comparing Quantum Convolutional Neural Networks and Quantum Support Vector Machines Slabbert, Donovan Lourens, Matt Petruccione, Francesco Quantum Physics Well-known quantum machine learning techniques, namely quantum kernel assisted support vector machines (QSVMs) and quantum convolutional neural networks (QCNNs), are applied to the binary classification of pulsars. In this comparitive study it is illustrated with simulations that both quantum methods successfully achieve effective classification of the HTRU-2 data set that connects pulsar class labels to eight separate features. QCNNs outperform the QSVMs with respect to time taken to train and predict, however, if the current NISQ era devices are considered and noise included in the comparison, then QSVMs are preferred. QSVMs also perform better overall compared to QCNNs when performance metrics are used to evaluate both methods. Classical methods are also implemented to serve as benchmark for comparison with the quantum approaches. |
| title | Pulsar Classification: Comparing Quantum Convolutional Neural Networks and Quantum Support Vector Machines |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2309.15592 |