Carbon emission prediction method and application system based on time sequence perception quantum LSTM

The invention relates to the technical field of deep learning algorithms and quantum computing, in particular to a carbon emission prediction method based on time sequence perception quantum LSTM and an application system. According to the quantum long-short-term memory network based on time sequence perception, firstly, gating attention mechanisms of different dimensions are adopted to carry out feature fusion extraction on time sequences and feature dimensions so as to capture important information on different dimensions and between the same dimension; then, information subjected to quantum coding is evolved through an unconstrained variable component sub-circuit composed of auxiliary quantum bits and characteristic quantum bits, and a quantum circuit is built by N quantum sub-gates; on the basis of building an assembly of an unconstrained variable component sub-circuit, introduction of an auxiliary quantum bit can help a model to capture a complex time sequence relation so as to realize a quantum circuit