30 citations · 58 across the 21 of their papers we have counts for
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Quantitative Universal Approximation for Noisy Quantum Neural Networks
Lukas Gonon, Antoine Jacquier, Marcel Mordarski
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…
Efficient Quantum Algorithm for Robust Training
Yue Wang, Guangyi He, Liepeng Zhang +2
Adversarial training is a standard defense against malicious input perturbations in security-critical machine-learning systems. Its main burden is structural: before every paramete…
Feedback-driven recurrent quantum neural network universality
Lukas Gonon, Rodrigo Martínez-Peña, Juan-Pablo Ortega
Quantum reservoir computing uses the dynamics of quantum systems to process temporal data, making it particularly well-suited for machine learning with noisy intermediate-scale qua…
Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs
Lukas Gonon, Antoine Jacquier
Universal approximation theorems are the foundations of classical neural networks, providing theoretical guarantees that the latter are able to approximate maps of interest. Recent…