Neural Fourier Surrogates for Data Reuploading Quantum Neural Networks

arXiv:2610.00841 · quant-ph, cs.LG · Submitted 2026-09-30 · Read on arXiv

quant-ph, cs.LG

Submitted: 2026-09-30

Updated: 2026-09-30

Comments: 10 pages, 5 figures, 2 tables

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 18× in this paper · explained in 1 episode
• Neural Fourier Surrogates — 15× in this paper
• Benchmark — 13× in this paper · explained in 2 episodes
• Random Fourier Features — 7× in this paper
• Regularization — 7× in this paper · explained in 3 episodes
• Ab Initio — 4× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• Neural Quantum States — 3× in this paper
• Generative AI — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes

Sources

• PennyLane: Automatic differentiation of hybrid quantum-classical computations
• Better than classical? The subtle art of benchmarking quantum machine learning models
• Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models
• Classically Approximating Variational Quantum Machine Learning with Random Fourier Features
• Decoupled Weight Decay Regularization
• Exploiting the equivalence between quantum neural networks and perceptrons
• On Dequantization of Supervised Quantum Machine Learning via Random Fourier Features

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