We propose a time series forecasting method named Quantum Gramian Angular Field (QGAF). This approach merges the advantages of quantum computing technology with deep learning, aiming to enhance the precision of time series classification and forecasting. We successfully transformed stock return time series data into two-dimensional images suitable for Convolutional Neural Network (CNN) training by designing specific quantum circuits. Distinct from the classical Gramian Angular Field (GAF) approach, QGAF's uniqueness lies in eliminating the need for data normalization and inverse cosine calculations, simplifying the transformation process from time series data to two-dimensional images. To validate the effectiveness of this method, we conducted experiments on datasets from three major stock markets: the China A-share market, the Hong Kong stock market, and the US stock market. Experimental results revealed that compared to the classical GAF method, the QGAF approach significantly improved time series prediction accuracy, reducing prediction errors by an average of 25\% for Mean Absolute Error (MAE) and 48\% for Mean Squared Error (MSE). This research confirms the potential and promising prospects of integrating quantum computing with deep learning techniques in financial time series forecasting.

我们提出了一种名为量子格拉米安角场的时间序列预测方法（QGAF）。该方法将量子计算技术与深度学习相结合，旨在提高时间序列分类和预测的精度。通过设计特定的量子电路，我们成功地将股票回报时间序列数据转化成适合卷积神经网络（CNN）训练的二维图像。与经典的格拉米安角场（GAF）方法不同，QGAF的独特之处在于消除了数据归一化和反余弦计算的需求，简化了从时间序列数据到二维图像的转化过程。通过在中国A股市场、香港股市和美国股市的数据集上进行实验证明了该方法的有效性。实验结果表明，与经典的GAF方法相比，QGAF方法在时间序列预测准确性方面具有显著改进，平均减少了25％的平均绝对误差（MAE）和48％的均方误差（MSE）。这项研究证实了在金融时间序列预测中，将量子计算与深度学习技术相结合的潜力和有前景的前景。

量子增强预测：利用量子Gramian角场和CNN进行股票收益预测