Traffic flow prediction plays a crucial role in the construction of smart cities. Although numerous models already exist for traffic flow prediction, they neither extracted the spatio-temporal features at different scales nor precisely considered the deviation between the traffic signals collected by the sensors and the trend signals at different scales. This leads to their inaccurate prediction results. To address the aforementioned issues, this paper proposes a multi-layer structure to extract spatio-temporal features at different scales and designs an information decomposition module to separate abnormal signals in traffic data. Furthermore, based on the above structure and modules, this paper constructs a new traffic flow prediction modelMulti-scaleAnomalyDecompositionGraphNeuralNetwork (MADGNN) for feature extraction and information decomposition at different scales. Firstly, the model encodes the input data to fully capture spatio-temporal dependencies. Then, this paper extracts spatio-temporal features at multiple scales based on a multi-layer structure containing multiple GRUs and subtracts the learned abnormal information from the input signal to achieve abnormal signal decomposition. Finally, we use multiple spatio-temporal hidden states for further information extraction and traffic prediction. The final prediction result of the model is obtained by adding up the prediction outputs of each layer. The experimental results show that, compared with DDGCRN, on the Railway datasets, the MAE and RMSE metrics are improved by an average of 3.92% and 2.30% respectively, and on the public dataset PEMSD8, the MAE and MAPE metrics are improved by 2.92% and 2.23% respectively.

Quantum circuit mapping (QCM) is a crucial preprocessing step for executing a logical circuit (LC) on noisy intermediate-scale quantum (NISQ) devices. Balancing the introduction of extra gates and the efficiency of preprocessing poses a significant challenge for the mapping process. To address this challenge, we propose the mutual-influence-aware (MIA) heuristic method by integrating an initial mapping search framework, an initial mapping generator, and a heuristic circuit mapper. Initially, the framework utilizes the generator to obtain a favorable starting point for the initial mapping search. With this starting point, the search process can efficiently discover a promising initial mapping within a few bidirectional iterations. The circuit mapper considers mutual influences of SWAP gates and is invoked once per iteration. Ultimately, the best result from all iterations is considered the QCM outcome. The experimental results on extensive benchmark circuits demonstrate that, compared to the iterated local search (ILS) method, which represents the current state-of-the-art, our MIA method introduces a similar number of extra gates while achieving nearly 95 times faster execution.