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Quantitative Universal Approximation for Noisy Quantum Neural Networks

Year: 2026

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2604.02064

Authors: Gonon, Lukas, Jacquier, Antoine, Mordarski, Marcel

Institution: University of Applied Sciences St. Gallen

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: UK Research and Innovation, Engineering and Physical Sciences Research Council

Awards: Rough Volatility: A Trojan horse into modern Financial computing, G460110269

OpenAlex ID: W7148823019

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