Quantitative Universal Approximation for Noisy Quantum Neural Networks
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866917510815678464 |
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| author | Gonon, Lukas Jacquier, Antoine Mordarski, Marcel |
| author_facet | Gonon, Lukas Jacquier, Antoine Mordarski, Marcel |
| contents | We provide here a universal approximation theorem with precise quantitative error bounds for noisy quantum neural networks. We focus on applications to Quantitative Finance, where target functions are often given as expectations. We further provide a detailed numerical analysis, testing our results on actual noisy quantum hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02064 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantitative Universal Approximation for Noisy Quantum Neural Networks Gonon, Lukas Jacquier, Antoine Mordarski, Marcel Quantum Physics Numerical Analysis Pricing of Securities 68Q12, 68T07, 65D15 We provide here a universal approximation theorem with precise quantitative error bounds for noisy quantum neural networks. We focus on applications to Quantitative Finance, where target functions are often given as expectations. We further provide a detailed numerical analysis, testing our results on actual noisy quantum hardware. |
| title | Quantitative Universal Approximation for Noisy Quantum Neural Networks |
| topic | Quantum Physics Numerical Analysis Pricing of Securities 68Q12, 68T07, 65D15 |
| url | https://arxiv.org/abs/2604.02064 |