发表机构
Kingston University London; Brunel University of London（伦敦金斯顿大学; 伦敦布鲁内尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究以GBP/USD即期汇率为案例，对1368种QNN配置开展大规模基准测试，发现门选择等对性能影响更大，最优配置R²达0.985且优于BiLSTM，还评估了IQM Emerald硬件噪声影响并给出架构指导。
AI 中文摘要
量子机器学习，尤其是量子神经网络（QNNs），是发展迅速且潜力日益增长的领域。尽管针对QNN配置的系统比较主要在分类任务中开展，但针对回归问题、尤其是金融时间序列预测的关注相对较少。本研究以英镑/美元（GBP/USD）即期汇率为案例，对用于金融时间序列预测的QNN组件配置开展大规模系统对比评估。通过对编码方法、拟设（ansatz）设计、量子比特数、层数及损失函数进行网格搜索，得到1368种不同的模型配置，每种配置均从预测精度、计算成本及收敛行为三个维度进行评估。结果揭示了方法选择对性能影响的独特见解，例如门的选择与排布对模型成功的重要性远超原始参数数量，且纠缠是整个电路的系统级属性，而非仅局限于拟设层面。表现最优的QNN配置取得了0.985的R²分数，优于经典双向长短期记忆网络（BiLSTM）基线模型。此外，通过在IQM Emerald量子硬件上执行评估了真实量子硬件噪声的影响，发现门错误与退相干是实际部署的重大障碍，门选择与电路深度被确定为硬件噪声鲁棒性的关键决定因素。总体而言，研究结果为QNN设计提供了实用的架构指导，并建立了近中期量子设备上QNN噪声敏感性的基线表征。
英文摘要
Quantum machine learning, and quantum neural networks (QNNs) in particular, are advancing fields with growing potential. Although systematic comparisons of QNN configurations have been explored primarily for classification tasks, comparatively little attention has been given to regression problems, particularly financial time series forecasting. This study presents a large-scale systematic comparative evaluation of QNN component configurations for financial time series forecasting, using the GBP/USD spot exchange rate as a case study. A grid search across encoding methods, ansatz designs, qubit counts, layer depths, and cost functions yields 1,368 distinct model configurations, each evaluated in terms of prediction accuracy, computational cost, and convergence behaviour. The results reveal unique insights into how the choice of methods influences performance, such as that gate selection and arrangement are more critical to model success than raw parameter count, and that entanglement is a system-level property of the full circuit rather than solely at the ansatz level. The best-performing QNN configuration achieves an $R^2$ score of 0.985, outperforming a classical BiLSTM baseline. Additionally, the impact of real quantum hardware noise is assessed through execution on the IQM Emerald device, revealing that gate errors and decoherence represent a significant barrier to practical deployment, with gate selection and circuit depth identified as key determinants of hardware noise resilience. Overall, the findings provide practical architectural guidance for QNN design and establish a baseline characterisation of QNN noise sensitivity on near-term quantum devices.
