Portfolio optimization is one of the most important financial problem, suffering from huge computational pressure due to arithmetic complexity. Quantum computing offers polynomial or even exponential speedup that turns out to be a promising approach. However, existing quantum methods is fundamentally limited by either poor scalability or insufficient accuracy. In this paper, we propose SAPO, which formally articulates the quantum circuit that seamlessly integrates financial theory and historical data characteristics with quantum algebra. The circuit design is extended from the HHL algorithm incorporating mean-variance theory, which promotes scalability by equivalent transformation. Then, we present a min-max eigenvalue model that leverages historical financial information to refine parameter settings with high accuracy. Experiments conducted on market data demonstrate that SAPO can effectively reduce the complexity by $\mathbf{3 6. 9 4 \%}$ compared to basic HHL [1], [2] and improve the accuracy by $1.46 \times$ compared to hybrid HHL [3].

This paper presents an improved Quantum Convolutional Neural Network (QCNN) architecture that overcomes key limitations of current quantum-classical hybrid models for image classification. To address challenges in expressivity and feature extraction on noisy intermediate-scale quantum (NISQ) devices, we propose two key innovations: (1) Gate Encoding Optimization, which reduces quantum bit (qubit) usage while retaining spatial information, and (2) Quantum Channel Attention (QCA), a novel attention mechanism tailored for hybrid QCNNs which utilizes measurement-driven feedback to capture inter-channel dependencies. Evaluated on the MNIST dataset, our model achieves 95.68 % accuracy—on par with classical CNNs (95.73 %) and significantly better than a baseline QCNN (93.23%)—while reducing parameters by 49.6 %. These results demonstrate the proposed model's effectiveness and practicality for quantum machine learning in the NISQ era.

Traditionally, agricultural forecasting has relied on empirical methods and basic statistical analysis, such as applying average values from previous years’ yields or using a simple linear fit for next year’s predictions. However, the emergence of data-driven approaches, particularly machine learning algorithms, has revolutionized yield prediction in agriculture. Machine learning techniques have demonstrated their potential to provide accurate predictions. However, existing models often rely on a limited number of input variables for crop yield predictions, which makes them only suitable for specific scenarios. In this study, we have developed four distinct machine learning-based predictors, incorporating various climate factors, including daytime temperature, nighttime temperature, precipitation (rainfall), vegetation index, and evapotranspiration as input variables to predict tomato acreage yields in counties of California, USA. Our results show that regression models constructed using neural networks and linear regression exhibited better performance than other predictors, achieving an average accuracy rate of 70% to 80%. Compared to most of the existing crop yield predictors, our models offer versatility while maintaining a desirable level of predictive accuracy. Expanding the number of input variables, such as nitrogen fertilizer usage etc, and introducing larger spatial and temporal high-resolution datasets for model training can improve our model performance, enabling us to obtain better results in tomato yield prediction.