VQEzy: An Open-Source Dataset for Parameter Initialization in Variational Quantum Eigensolvers
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915516822585344 |
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| author | Zhang, Chi Zheng, Mengxin Lou, Qian Leung, Hui Min Chen, Fan |
| author_facet | Zhang, Chi Zheng, Mengxin Lou, Qian Leung, Hui Min Chen, Fan |
| contents | Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms, whose performance is highly sensitive to parameter initialization. Although recent machine learning-based initialization methods have achieved state-of-the-art performance, their progress has been limited by the lack of comprehensive datasets. Existing resources are typically restricted to a single domain, contain only a few hundred instances, and lack complete coverage of Hamiltonians, ansatz circuits, and optimization trajectories. To overcome these limitations, we introduce VQEzy, the first large-scale dataset for VQE parameter initialization. VQEzy spans three major domains and seven representative tasks, comprising 12,110 instances with full VQE specifications and complete optimization trajectories. The dataset is available online, and will be continuously refined and expanded to support future research in VQE optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17322 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VQEzy: An Open-Source Dataset for Parameter Initialization in Variational Quantum Eigensolvers Zhang, Chi Zheng, Mengxin Lou, Qian Leung, Hui Min Chen, Fan Machine Learning Emerging Technologies Quantum Physics Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms, whose performance is highly sensitive to parameter initialization. Although recent machine learning-based initialization methods have achieved state-of-the-art performance, their progress has been limited by the lack of comprehensive datasets. Existing resources are typically restricted to a single domain, contain only a few hundred instances, and lack complete coverage of Hamiltonians, ansatz circuits, and optimization trajectories. To overcome these limitations, we introduce VQEzy, the first large-scale dataset for VQE parameter initialization. VQEzy spans three major domains and seven representative tasks, comprising 12,110 instances with full VQE specifications and complete optimization trajectories. The dataset is available online, and will be continuously refined and expanded to support future research in VQE optimization. |
| title | VQEzy: An Open-Source Dataset for Parameter Initialization in Variational Quantum Eigensolvers |
| topic | Machine Learning Emerging Technologies Quantum Physics |
| url | https://arxiv.org/abs/2509.17322 |