量子神经网络可跨任务迁移，用少量参数预测能耗、交通等复杂数据。
A Quantum Neural Network Transfer-Learning Model for Forecasting Problems with Continuous and Discrete Variables
- 用2量子比特和8个可训练参数构建连续变量量子模型，实现快速迁移。
- 在库尔德斯坦负荷数据上训练后，直接用于能源、交通等6类预测任务且效果良好。
- 首次对比连续与离散量子模型，证明连续型更适合多场景时间序列预测。
本研究提出一种简单高效的连续与离散变量量子神经网络（QNN）模型，作为预测任务的迁移学习方法。连续变量量子神经网络（CV-QNN）采用单层结构，包含两个量子比特，通过位移、旋转、分束器、压缩及非高斯立方相门等最少数量的量子门实现纠缠，最多仅含8个可训练参数。该模型核心优势在于仅需在一个数据集上训练，即可将学习到的参数冻结并迁移到其他预测任务中，几乎无需微调。初始在库尔德斯坦负荷需求数据集上训练后，其冻结参数成功应用于能源消耗、交通流量、天气状况及加密货币价格预测等多个任务，表现优异。此外，研究还提出了等效的2线与4线离散变量量子模型，并进行了性能评估，结果表明其表现良好但相对连续变量模型有所下降。
原文摘要 · Abstract (English)
This study introduces simple yet effective continuous- and discrete-variable quantum neural network (QNN) models as a transfer-learning approach for forecasting tasks. The CV-QNN features a single quantum layer with two qubits to establish entanglement and utilizes a minimal set of quantum gates, including displacement, rotation, beam splitter, squeezing, and a non-Gaussian cubic-phase gate, with a maximum of eight trainable parameters. A key advantage of this model is its ability to be trained on a single dataset, after which the learned parameters can be transferred to other forecasting problems with little to no fine-tuning. Initially trained on the Kurdistan load demand dataset, the model's frozen parameters are successfully applied to various forecasting tasks, including energy consumption, traffic flow, weather conditions, and cryptocurrency price prediction, demonstrating strong performance. Furthermore, the study introduces a discrete-variable quantum model with an equivalent 2- and 4-wire configuration and presents a performance assessment, showing good but relatively lower effectiveness compared to the continuous-variable model.
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。