Provides a quantitative universal approximation theorem with error bounds for noisy quantum neural networks and tests on real hardware for quantitative finance.
rep., Quantinuum, 2025
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Quantitative Universal Approximation for Noisy Quantum Neural Networks
Provides a quantitative universal approximation theorem with error bounds for noisy quantum neural networks and tests on real hardware for quantitative finance.