Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913916988162048 |
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| author | Wakaura, Hikaru Mulyawan, Rahmat Suksmono, Andriyan B. |
| author_facet | Wakaura, Hikaru Mulyawan, Rahmat Suksmono, Andriyan B. |
| contents | Quantum imaginary time evolution (QITE) is a powerful method to derive the ground states of the systems. Only the damping of quantum states leads it; hence, reaching the ground state is guaranteed by nature without any external manipulation. Numerous QITE methods by many groups are used to improve speed and accuracy, derive excited states, and solve combined optimization problems. However, the QITE methods have not been used for quantum machine learning to predict the ideal values for multiple input values. Therefore, we propose a method for applying QITE methods for quantum machine learning and demonstrate fitting problems of elementary functions and classification problems on a 2-D plane. As a result, we confirmed that our method was more accurate than a quantum neural network in solving some problems. Our method can be used for other quantum machine learning algorithms; hence, it may be the milestone for applying QITE to quantum machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22801 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network Wakaura, Hikaru Mulyawan, Rahmat Suksmono, Andriyan B. Quantum Physics Quantum imaginary time evolution (QITE) is a powerful method to derive the ground states of the systems. Only the damping of quantum states leads it; hence, reaching the ground state is guaranteed by nature without any external manipulation. Numerous QITE methods by many groups are used to improve speed and accuracy, derive excited states, and solve combined optimization problems. However, the QITE methods have not been used for quantum machine learning to predict the ideal values for multiple input values. Therefore, we propose a method for applying QITE methods for quantum machine learning and demonstrate fitting problems of elementary functions and classification problems on a 2-D plane. As a result, we confirmed that our method was more accurate than a quantum neural network in solving some problems. Our method can be used for other quantum machine learning algorithms; hence, it may be the milestone for applying QITE to quantum machine learning. |
| title | Optimization by VarQITE on Adaptive Variational Quantum Kolmogorov-Arnold Network |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2506.22801 |