Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting

Key points

• 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.1
• 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.1
• 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.1
• Overall, the findings provide practical architectural guidance for QNN design and establish a baseline characterisation of QNN noise sensitivity on near-term quantum devices.1

Sources (1)

• [1]Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 03:32 PM

  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.

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