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Quantum Machine Learning
Quantitative Universal Approximation for Noisy Quantum Neural Networks
arXiv
Authors: Lukas Gonon, Antoine Jacquier, Marcel Mordarski
Year
2026
Paper ID
38864
Status
Preprint
Abstract Read
~2 min
Abstract Words
47
Citations
N/A
Abstract
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2026 reference point for readers tracking recent quantum research.
- We provide here a universal approximation theorem with precise quantitative error bounds for noisy quantum neural networks.
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