QuFoundry: Generating Data with Quantum Properties for Quantum Machine Learning Utility
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910064152936448 |
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| author | Ludmir, Jason Martin, Ian DiBrita, Nicholas S. Patel, Tirthak |
| author_facet | Ludmir, Jason Martin, Ian DiBrita, Nicholas S. Patel, Tirthak |
| contents | Quantum machine learning (QML) promises significant speedups, particularly when operating on quantum datasets. However, its progress is hindered by the scarcity of suitable training data. Existing synthetic data generation methods fall short in capturing essential entanglement properties, limiting their utility for QML. To address this, we introduce QuFoundry, a low-depth quantum data generation framework that produces entangled, high-quality samples emulating diverse classical and quantum distributions, enabling more effective development and evaluation of QML models in representative-data settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21622 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QuFoundry: Generating Data with Quantum Properties for Quantum Machine Learning Utility Ludmir, Jason Martin, Ian DiBrita, Nicholas S. Patel, Tirthak Emerging Technologies Quantum machine learning (QML) promises significant speedups, particularly when operating on quantum datasets. However, its progress is hindered by the scarcity of suitable training data. Existing synthetic data generation methods fall short in capturing essential entanglement properties, limiting their utility for QML. To address this, we introduce QuFoundry, a low-depth quantum data generation framework that produces entangled, high-quality samples emulating diverse classical and quantum distributions, enabling more effective development and evaluation of QML models in representative-data settings. |
| title | QuFoundry: Generating Data with Quantum Properties for Quantum Machine Learning Utility |
| topic | Emerging Technologies |
| url | https://arxiv.org/abs/2509.21622 |