arXiv：cs.LG· Jack Waller, Xing Liang, Dimitrios Makris, Rajagopal Nilavalan·· 4 小时前
量子神经网络配置的大规模基准测试：面向金融时间序列预测
Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting
AI 导读
一项研究用 GBP/USD 即期汇率对量子神经网络（QNN）组件配置进行大规模系统评测，网格搜索编码方式、ansatz 设计、量子比特数、层深与代价函数，共生成 1,368 种模型配置。
正文
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, 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.
| Subjects: | Machine Learning (cs.LG); Quantum Physics (quant-ph) |
| Cite as: | arXiv:2610.12148 [cs.LG] |
| (or arXiv:2610.12148v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12148 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jack Waller [view email]
[v1]
Thu, 8 Oct 2026 15:32:24 UTC (7,276 KB)
来源：arXiv：cs.LG · arxiv.org