Abstract: Rapid and accurate measurement of effluent chemical oxygen demand (COD) was essential for the dynamic regulation of water quality in wastewater treatment processes. In order to solve the problem of real-time detection of COD in the effluent, a COD prediction method based on quantum weighted minimal gate unit (QWMGU) neural network was proposed. The time series was first constructed through a multi-dimensional single-step (sliding window) prediction technique; then quantum computing mechanism was designed in the links of forgetting gate, candidate state and output of the minimal gated unit (MGU). The network neurons were endowed with quantum characteristics by updating the quantum phase shift gate matrix instead of the MGU weight matrix, and the specific rules and construction process of the QWMGU model design were given. The method was applied to the prediction of effluent COD of the wastewater treatment plant in Dezhou City in 2020 and compared with five usual prediction models to test the model's superiority. The results showed that the relative prediction error of the QWMGU network was better than other methods and more stable, with the root mean square error, coefficient of determination and mean absolute error of 0.073, 1 and 0.047, respectively. The model helped to achieve efficient online detection of COD in wastewater treatment plants.