发表机构
IonQ Inc.（IonQ公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出神经傅里叶代理模型（NFS），一种随机经典神经网络，用于在相同傅里叶级数支撑上学习系数，作为评估数据重上传量子神经网络性能的自然经典基线，并在表格基准上展现出竞争力。
AI 中文摘要
对于量子机器学习而言，经典与量子优势之间的确切界限仍未被充分理解。量子神经网络（QNN）与现有经典模型之间的直接比较，由于两者涵盖根本不同的函数类别，往往无法为二者差异提供更广泛的洞见。受神经量子态和随机傅里叶特征技术的启发，本工作引入了神经傅里叶代理模型（NFS），这是一种随机经典神经网络架构，用于在量子神经网络所支持的相同有限傅里叶级数支撑集上高效学习系数。在一系列表格基准数据集上的测试表明，NFS是一种有效的分类器架构，广泛地与已建立的经典基线（包括一个可比的随机傅里叶特征模型）竞争，并具有与数据重上传QNN相当的性能；结合对合成数据上QNN和NFS学习到的傅里叶谱的额外比较分析，这些结果确立了NFS作为评估QNN性能的自然经典基线。
英文摘要
For quantum machine learning, the exact boundary between classical and quantum advantage is still poorly understood. Direct comparison between quantum neural networks (QNNs) and existing classical models, which encompass fundamentally different function classes, often fails to provide broader insight into the difference between the two. Inspired by the techniques of Neural Quantum States and Random Fourier Features, this work introduces Neural Fourier Surrogates (NFS), a stochastic classical neural network architecture for efficiently learning coefficients over the same finite Fourier series support as quantum neural networks. Testing on a selection of tabular benchmark datasets, we find that NFS is an effective classifier architecture broadly competitive with established classical baselines, including a comparable Random Fourier Features model, and possessing comparable performance to data-reuploading QNNs; combined with additional analysis comparing the learned Fourier spectra of QNNs and NFS on synthetic data, these results establish NFS as a natural classical baseline for evaluating QNN performance.
Comments10 pages, 5 figures, 2 tables
