在稀缺和含噪数据上对量子代理模型的基准测试
Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
AI总结：
针对稀缺含噪数据下经典代理模型精度不足的问题，首次将量子神经网络作为代理模型应用于高维真实数据，实证表明其优于参数量相近的经典人工神经网络，并评估了当前NISQ硬件性能与所需门保真度。
摘要：
代理模型在工业界和学术界被广泛用于高效逼近给定的黑盒函数。由于经典机器学习中的最先进方法在处理实际应用中常见的稀缺和含噪数据集时，往往难以准确解决该问题，因此研究新方法备受关注。受近期理论结果启发——这些结果表明量子神经网络（QNNs）在存在稀缺和含噪数据时有望超越其经典对应物，我们通过实证基准测试了它们在此场景下的定性性能。我们的工作展示了首个以应用为中心的方法，将QNNs作为代理模型应用于更高维度的真实世界数据。与具有相似参数量的经典人工神经网络相比，我们的QNN在处理含噪和稀缺数据时表现出显著更优的结果，从而激励未来工作探索代理建模中的这一潜在量子优势。最后，我们通过实验展示了当前NISQ硬件的性能，并估计了复现我们仿真结果所需的门保真度。
英文摘要：
Surrogate models are ubiquitously used in industry and academia to efficiently approximate given black box functions. As state-of-the-art methods from classical machine learning frequently struggle to solve this problem accurately for the often scarce and noisy data sets in practical applications, investigating novel approaches is of great interest. Motivated by recent theoretical results indicating that quantum neural networks (QNNs) have the potential to outperform their classical analogs in the presence of scarce and noisy data, we benchmark their qualitative performance for this scenario empirically. Our contribution displays the first application-centered approach of using QNNs as surrogate models on higher dimensional, real world data. When compared to a classical artificial neural network with a similar number of parameters, our QNN demonstrates significantly better results for noisy and scarce data, and thus motivates future work to explore this potential quantum advantage in surrogate modelling. Finally, we demonstrate the performance of current NISQ hardware experimentally and estimate the gate fidelities necessary to replicate our simulation results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
