A quantum computing-based approach to improve the efficiency of library information retrieval

Since the digital information resources have been characterized by explosive growth, the traditional Bayesian algorithm has been unable to meet the demands of retrieval scenarios for large amounts of resource data. Based on the traditional Bayesian network, this paper proposes a quantum Bayesian network structure using a quantum probabilistic graph model based on the density algorithm to integrate the dispersed quantum state information. A digital library information retrieval system is designed with user request, information retrieval processing and retrieval results as the core modules. Taking the (quantum) Bayesian network as the inference machine, the user request is processed through semantic logic reasoning and extraction, and the inference and retrieval are combined with the probability estimation results. Overall, the retrieval efficiency of the library information retrieval system based on Quantum Bayesian Networks for different types of queries is consistently 80.00% and above. The quantum Bayesian network provides more levels of probabilistic reasoning based on the uniqueness of quantum computing, which effectively improves the efficiency of library information retrieval.