量子与经典模型联合纠错，提升时间序列预测精度。
Quantum-classical hybrid models based on error correction for time series forecasting
- 量子模型捕捉新模式，经典模型修正量子误差。
- 在多数任务中表现优于纯经典或经典-经典混合模型。
- 为量子模型融入传统预测框架提供新思路。
时间序列预测通过结合不同模型的优势获益良多，尤其在一种由模型修正另一模型的方案中，可通过捕捉预测误差中的补充模式来提升性能。与此同时，量子模型借助量子现象，在混合架构中与经典模型协同，增强了经典模型的能力。本文首次提出基于误差校正的量子-经典混合预测系统：量子模型首先利用量子特性提取模式，经典模型则从量子模型的预测误差中捕捉剩余模式。相较于单一经典模型及经典的误差校正混合模型，该量子-经典系统因互补能力在多数问题上取得了最佳结果。本工作为将量子模型引入成熟的时间序列预测混合框架铺平了道路。
原文摘要 · Abstract (English)
Time series forecasting largely benefits from combining the strengths of different models, especially using a scheme where a model corrects another model by capturing supplementary patterns from forecasting errors. Concurrently, quantum models are providing a means to augment the classical capacity, including in time series forecasting, by acting alongside classical models in hybrid architectures. In this work, we propose the first forecasting system based on error correction that jointly uses quantum and classical models. Here, quantum models first extract patterns by exploring quantum phenomena, and classical models capture the remaining patterns from the quantum errors. Compared to classical single models and classical-classical hybrid models based on error correction, the complementary capacity that emerges from this quantum-classical system provided the best results in most of the addressed problems. Therefore, this work paves the way to introduce quantum models in established hybridization schemes for time series forecasting.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。