← Back to papers
Quantitative Universal Approximation for Noisy Quantum Neural Networks
Lukas Gonon, Antoine Jacquier, Marcel Mordarski·April 2, 2026
Quantum Physicsmath.NAq-fin.PR
AI Breakdown
Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.
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.