English(EN) Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting

量子神经网络在金融预测方面展现出潜力，但硬件噪声仍是障碍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

一项新近发表在arXiv上的研究，对用于金融时间序列预测的各种量子神经网络（QNN）配置进行了基准测试，具体使用了英镑/美元汇率。该研究探索了1,368种不同的QNN配置，评估了预测准确性、计算成本和收敛性。研究结果表明，门的选择和排列比参数数量更关键，而纠缠是一种系统级属性。表现最佳的QNN达到了0.985的R^2分数，超过了经典的BiLSTM基线。然而，在IQM Emerald量子设备上的执行显示，硬件噪声（包括门错误和退相干）对实际部署构成了重大挑战。 AI

影响为量子神经网络设计提供了架构指导，并强调了近期量子设备上的噪声敏感性。

排序理由学术论文，详细介绍了研究发现和基准测试。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Jack Waller, Xing Liang, Dimitrios Makris, Rajagopal Nilavalan · 2026-10-09 04:00

面向金融时间序列预测的大规模量子神经网络配置基准测试

arXiv:2610.12148v1 Announce Type: new Abstract: 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, co…

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面向金融时间序列预测的大规模量子神经网络配置基准测试

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