Enhanced quantum long short-term memory by using bidirectional ring variational quantum circuit

With the rapid development of quantum machine learning, the Quantum Long Short-Term Memory (QLSTM) has been found to exhibit faster convergence characteristics in time series prediction problems. Currently, it has found applications in fields such as financial market analysis, natural language processing, and weather forecasting, but its accuracy in regression problem prediction still shows deficiencies. To improve the prediction accuracy of the model, enhanced quantum long short-term memory by using bidirectional ring variational quantum circuit (EQLSTM) is proposed. We reconsidered and optimized the types, quantities, and arrangements of quantum gates, designing a bidirectional ring variational quantum circuit composed of CRX gates (Bi-VQC) to enhance its expressibility. Bi-VQC can more accurately describe and encode complex quantum state features, enabling the EQLSTM to better learn and represent the characteristics of the input data. In addition, Bi-VQC reduces the number of optimizable parameters and lowers the circuit load of the EQLSTM model. To validate the effectiveness of the proposed model, experiments are conducted to test and evaluate the EQLSTM using the aggregated traffic in the UK academic network backbone. The experimental results confirm that the proposed EQLSTM model improves from 90.56% to 97.57% compared to the QLSTM model.