Enhanced Variational Quantum Kolmogorov-Arnold Network
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909967983837184 |
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| author | Wakaura, Hikaru Mulyawan, Rahmat Suksmono, Andriyan B. |
| author_facet | Wakaura, Hikaru Mulyawan, Rahmat Suksmono, Andriyan B. |
| contents | The Kolmogorov-Arnold Network (KAN) is a novel multi-layer network model recognized for its efficiency in neuromorphic computing, where synapses between neurons are trained linearly. Computations in KAN are performed by generating a polynomial vector from the state vector and layer-wise trained synapses, enabling efficient processing. While KAN can be implemented on quantum computers using block encoding and Quantum Signal Processing, these methods require fault-tolerant quantum devices, making them impractical for current Noisy Intermediate-Scale Quantum (NISQ) hardware. We propose the Enhanced Variational Quantum Kolmogorov-Arnold Network (EVQKAN) to overcome this limitation, which emulates KAN through variational quantum algorithms. The EVQKAN ansatz employs a tiling technique to emulate layer matrices, leading to significantly higher accuracy compared to conventional Variational Quantum Kolmogorov-Arnold Network (VQKAN) and Quantum Neural Networks (QNN), even with a smaller number of layers. EVQKAN achieves superior performance with a single-layer architecture, whereas QNN and VQKAN typically struggle. Additionally, EVQKAN eliminates the need for Quantum Signal Processing, enhancing its robustness to noise and making it well-suited for practical deployment on NISQ-era quantum devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22604 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhanced Variational Quantum Kolmogorov-Arnold Network Wakaura, Hikaru Mulyawan, Rahmat Suksmono, Andriyan B. Quantum Physics Computational Physics The Kolmogorov-Arnold Network (KAN) is a novel multi-layer network model recognized for its efficiency in neuromorphic computing, where synapses between neurons are trained linearly. Computations in KAN are performed by generating a polynomial vector from the state vector and layer-wise trained synapses, enabling efficient processing. While KAN can be implemented on quantum computers using block encoding and Quantum Signal Processing, these methods require fault-tolerant quantum devices, making them impractical for current Noisy Intermediate-Scale Quantum (NISQ) hardware. We propose the Enhanced Variational Quantum Kolmogorov-Arnold Network (EVQKAN) to overcome this limitation, which emulates KAN through variational quantum algorithms. The EVQKAN ansatz employs a tiling technique to emulate layer matrices, leading to significantly higher accuracy compared to conventional Variational Quantum Kolmogorov-Arnold Network (VQKAN) and Quantum Neural Networks (QNN), even with a smaller number of layers. EVQKAN achieves superior performance with a single-layer architecture, whereas QNN and VQKAN typically struggle. Additionally, EVQKAN eliminates the need for Quantum Signal Processing, enhancing its robustness to noise and making it well-suited for practical deployment on NISQ-era quantum devices. |
| title | Enhanced Variational Quantum Kolmogorov-Arnold Network |
| topic | Quantum Physics Computational Physics |
| url | https://arxiv.org/abs/2503.22604 |