量子增强气象预测：孟加拉国天气预测系统的混合量子机器学习实践

This post introduces the QuantWeather-BD project, a hybrid quantum machine learning system designed for Bangladesh's weather prediction. It combines variational quantum circuits (VQC) with classical neural networks, uses 45 years of NASA POWER data for training, and provides real-time next-day weather forecasts via a Telegram bot. The project aims to address local challenges like data scarcity and limited computing resources in weather prediction.

Bangladesh, affected by monsoon climate, relies heavily on accurate weather forecasts for agriculture, fishing, and disaster prevention. However, traditional methods face issues like data scarcity, high compute costs, and difficulty capturing complex non-linear relationships in high-dimensional meteorological data. Quantum computing's superposition and entanglement properties offer potential to tackle these challenges, leading to the development of QuantWeather-BD.

QuantWeather-BD uses a hybrid quantum-classical architecture. The variational quantum circuit (VQC) component—composed of parameterized quantum gates—learns high-dimensional feature representations (e.g., correlations between temperature, humidity, and pressure). The output of VQC is fed into a classical neural network, which handles final prediction and ensures system stability. This design leverages quantum advantages in feature extraction while maintaining practicality via classical components.

The project uses 45 years of NASA POWER dataset covering Bangladesh, including variables like temperature (max/min/avg), humidity, pressure, precipitation, and wind. Data preprocessing steps include: time-series interpolation for missing values, statistical outlier detection, feature normalization, and sliding window construction for time dependencies. Training is done in stages: pre-train classical neural network first, then joint training of VQC and classical network using hybrid optimization to avoid local optima.

QuantWeather-BD is deployed as a Telegram bot, allowing users to get next-day forecasts via commands (temperature range, precipitation probability, humidity, wind, comfort index). It integrates OpenWeatherMap API for real-time observation data, ensuring predictions are based on latest inputs. The bot runs on cloud servers with optimized quantum model inference, requiring no dedicated quantum hardware for accessibility.

Key challenges and solutions:

1. Quantum noise: Zero noise extrapolation, symmetry protection, classical post-processing for error mitigation.
2. Hybrid optimization convergence: Adaptive learning rate (large initial, then reduced) and momentum to speed convergence.
3. Data scarcity: Satellite-ground data fusion and transfer learning (pre-train with global data, fine-tune with local data) to improve coverage.

The system benefits agriculture (adjust farming decisions to reduce weather risks) and has potential for extreme weather disaster warnings. As quantum hardware advances, the hybrid architecture can scale to increase quantum component complexity, further improving prediction accuracy. This project demonstrates the feasibility of quantum machine learning in practical weather prediction for developing regions.