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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.11002 |
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| _version_ | 1866914368882475008 |
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| author | Kim, Minwoo Park, Kyoung Keun Jeong, Uihwan Lee, Sangyeon Kim, Taehyun |
| author_facet | Kim, Minwoo Park, Kyoung Keun Jeong, Uihwan Lee, Sangyeon Kim, Taehyun |
| contents | We propose the unitary variational quantum-neural hybrid eigensolver (U-VQNHE), which improves upon the original VQNHE by enforcing unitary neural transformations. The non-unitary nature of VQNHE causes normalization issues and divergence of the loss function during training, leading to exponential scaling of measurement overhead with qubit number. U-VQNHE resolves these issues, significantly reduces required measurements, and retains improved accuracy and stability over standard variational quantum eigensolvers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A scalable quantum-neural hybrid variational algorithm for ground state estimation Kim, Minwoo Park, Kyoung Keun Jeong, Uihwan Lee, Sangyeon Kim, Taehyun Quantum Physics We propose the unitary variational quantum-neural hybrid eigensolver (U-VQNHE), which improves upon the original VQNHE by enforcing unitary neural transformations. The non-unitary nature of VQNHE causes normalization issues and divergence of the loss function during training, leading to exponential scaling of measurement overhead with qubit number. U-VQNHE resolves these issues, significantly reduces required measurements, and retains improved accuracy and stability over standard variational quantum eigensolvers. |
| title | A scalable quantum-neural hybrid variational algorithm for ground state estimation |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2507.11002 |